Miracle-dz/UniFAR-FTUs huggingface dataset:
Bushnell-Kutzko types for $P$-ordinary automorphic representations on unitary groups

This paper generalizes a theorem of Hida on the structure of ordinary representations on unitary groups to $P$-ordinary representations, where $P$ is a general parabolic subgroup of some general linear group. When $P$ is minimal, we recover Hida's theorem which asserts that ordinary subspaces are 1-dimensional. While analogous $P$-ordinary subspaces are infinite-dimensional in general, we use the theory of Bushnell-Kutzko types to canonically associate a finite-dimensional type to the representation (under minor assumptions) that has multiplicity one in its $P$-ordinary subspace. We simultaneous develop the theory of modular forms on unitary groups with $P$-Iwahoric level structure whose nebentypus is a type (instead of a character) and construct lattices of $P$-ordinary cuspidal forms inside $P$-ordinary automorphic representations. We also obtain direct consequences for the dual notion of $P$-anti-ordinary forms and representations.
Ultrasound Image Segmentation of Thyroid Nodule via Latent Semantic Feature Co-Registration

Segmentation of nodules in thyroid ultrasound imaging plays a crucial role in the detection and treatment of thyroid cancer. However, owing to the diversity of scanner vendors and imaging protocols in different hospitals, the automatic segmentation model, which has already demonstrated expert-level accuracy in the field of medical image segmentation, finds its accuracy reduced as the result of its weak generalization performance when being applied in clinically realistic environments. To address this issue, the present paper proposes ASTN, a framework for thyroid nodule segmentation achieved through a new type co-registration network. By extracting latent semantic information from the atlas and target images and utilizing in-depth features to accomplish the co-registration of nodules in thyroid ultrasound images, this framework can ensure the integrity of anatomical structure and reduce the impact on segmentation as the result of overall differences in image caused by different devices. In addition, this paper also provides an atlas selection algorithm to mitigate the difficulty of co-registration. As shown by the evaluation results collected from the datasets of different devices, thanks to the method we proposed, the model generalization has been greatly improved while maintaining a high level of segmentation accuracy.
Ion and electron characteristics in auroral density cavities associated with ion beams: No evidence for cold ionospheric plasma

Low-density cavities associated with upgoing ion beams were identified in the auroral regions over two decades ago. In order to understand the waves, double layers, and solitary structures observed within these cavities, accurate measurements of the plasma distribution function are required. Although measurements by DE 1 indicated that these cavities were composed primarily of hot plasma in the form of ion beams, plasma sheet ions, and inverted V electrons, later reports from Viking showed these cavities contained a cold plasma component whose density was an order of magnitude larger than the hot component. Recent measurements by the FAST satellite contrast sharply with the Viking results and support the earlier DE 1 observations. Regions of upgoing ion beams observed by FAST are shown to contain little or no cold plasma. The hot electron densities (>100 eV) and the combined plasma sheet ion and upgoing ion beam densities (>30 eV) agree remarkably well. Furthermore, no cold ions (0–30 eV) are measured at low energies by the mass spectrometer, which precludes the presence of significant (>20%) cold electrons to preserve charge neutrality. Characteristics of the plasma for 11 ion beam events are tabulated.
Theoretical strong line metallicity diagnostics for the JWST era

The ratios of strong rest-frame optical emission lines are the dominant indicator of metallicities in high-redshift galaxies. Since typical strong-line based metallicity indicators are calibrated on auroral lines at $z=0$, their applicability for galaxies in the distant Universe is unclear. In this paper, we make use of mock emission line data from cosmological simulations to investigate the calibration of rest-frame optical emission lines as metallicity indicators at high redshift. Our model, which couples the SIMBA cosmological galaxy formation simulation with cloudy photoionization calculations, includes contributions from HII regions, post-AGB stars and Diffuse Ionized Gas (DIG). We find mild redshift evolution in the 12 indicators that we study, which implies that the dominant physical properties that evolve in our simulations do have a discernible impact on the metallicity calibrations at high redshifts. When comparing our calibrations with high redshift auroral line observations from James Webb Space Telescope we find a slight offset between our model results and the observations and find that a higher ionization parameter at high redshifts can be one of the possible explanations. We explore the physics that drives the shapes of strong-line metallicity relationships and propose calibrations for hitherto unexplored low-metallicity regimes. Finally, we study the contribution of DIG to total line fluxes. We find that the contribution of DIG increases with metallicity at z $\sim$ 0 for singly ionized oxygen and sulfur lines and can be as high as 70% making it crucial to include their contribution when modeling nebular emission.
Exploration with Principles for Diverse AI Supervision

Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI. While this generative AI approach has produced impressive results, it heavily leans on human supervision. Even state-of-the-art AI models like ChatGPT depend on fine-tuning through human demonstrations, demanding extensive human input and domain expertise. This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation. To address this limitation, we propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data. Drawing inspiration from unsupervised reinforcement learning (RL) pretraining, EAI achieves exploration within the natural language space. We accomplish this by harnessing large language models to assess the novelty of generated content. Our approach employs two key components: an actor that generates novel content following exploration principles and a critic that evaluates the generated content, offering critiques to guide the actor. Empirical evaluations demonstrate that EAI significantly boosts model performance on complex reasoning tasks, addressing the limitations of human-intensive supervision.
Accuracy Requirements: Assessing the Importance of First Post-Adiabatic Terms for Small-Mass-Ratio Binaries

We investigate the impact of post-adiabatic (1PA) terms on parameter estimation for extreme and intermediate mass-ratio inspirals using state-of-the-art waveform models. Our analysis is the first to employ Bayesian inference to assess systematic errors for 1PA waveforms. We find that neglecting 1PA terms introduces significant biases for the (small) mass ratio $\epsilon \gtrsim 10^{-5}$ for quasi circular orbits in Schwarzschild spacetime, which can be mitigated with resummed 3PN expressions at 1PA order. Moreover, we show that the secondary spin is strongly correlated with the other intrinsic parameters, and it can not be constrained for $\epsilon \lesssim 10^{-5}$. Finally, we highlight the need for addressing eccentric waveform systematics in the small-mass-ratio regime, as they yield stronger biases than the circular limit in both intrinsic and extrinsic parameters.
Improving power-grid systems via topological changes, or how self-organized criticality can help stability

Cascade failures in power grids occur when the failure of one component or subsystem causes a chain reaction of failures in other components or subsystems, ultimately leading to a widespread blackout or outage. Controlling cascade failures on power grids is important for many reasons like economic impact, national security, public safety and even rippled effects like troubling transportation systems. Monitoring the networks on node level has been suggested by many, either controlling all nodes of a network or by subsets. We identify sensitive graph elements of the weighted European power-grids (from 2016, 2022) by two different methods. We determine bridges between communities and point out"weak"nodes by the lowest local synchronization of the swing equation. In the latter case we add bypasses of the same number as the bridges at weak nodes and we compare the synchronization, cascade failure behavior by the dynamical improvement with the purely topological changes. We also compare the results on bridge removed networks, similar to islanding, and with the addition of links at randomly selected places. The synchronization improves the best by the bypassing, while the average cascade sizes are the lowest with bridge additions. However, for very large or small global couplings these network changes do not help, they seem to be useful near the synchronization transition region, where self-organization drives the power-grid. Thus, we provide a demonstration for the Braess' Paradox on continental sized power grid simulations and uncover the limitations of this phenomenon. We also determine the cascade size distributions and justify the power-law tails near the transition point on these grids.
Tsunami-driven ionospheric perturbations associated with the 2011 Tohoku earthquake as detected by subionospheric VLF signals

The subionospheric data from a Japanese very low frequency/low frequency (VLF/LF) receiving station at Moshiri, Hokkaido, are used to detect the response of the lower ionosphere to the tsunami triggered by the 2011 Tohoku earthquake. Disturbances in the phase and amplitude of VLF signals propagating from the transmitter in Hawaiian Islands are observed during the tsunami wave passage, and these effects in the ionosphere are compared to the in situ sea-level global positioning system (GPS) measurements near Japan. The frequency of the maximum spectral amplitude both for the VLF and GPS data is found to be in the range of periods of 8–50 min, which is likely to correspond to the period of the internal gravity waves generated by the tsunami.
Adam-family Methods with Decoupled Weight Decay in Deep Learning

In this paper, we investigate the convergence properties of a wide class of Adam-family methods for minimizing quadratically regularized nonsmooth nonconvex optimization problems, especially in the context of training nonsmooth neural networks with weight decay. Motivated by the AdamW method, we propose a novel framework for Adam-family methods with decoupled weight decay. Within our framework, the estimators for the first-order and second-order moments of stochastic subgradients are updated independently of the weight decay term. Under mild assumptions and with non-diminishing stepsizes for updating the primary optimization variables, we establish the convergence properties of our proposed framework. In addition, we show that our proposed framework encompasses a wide variety of well-known Adam-family methods, hence offering convergence guarantees for these methods in the training of nonsmooth neural networks. More importantly, we show that our proposed framework asymptotically approximates the SGD method, thereby providing an explanation for the empirical observation that decoupled weight decay enhances generalization performance for Adam-family methods. As a practical application of our proposed framework, we propose a novel Adam-family method named Adam with Decoupled Weight Decay (AdamD), and establish its convergence properties under mild conditions. Numerical experiments demonstrate that AdamD outperforms Adam and is comparable to AdamW, in the aspects of both generalization performance and efficiency.
Strength of the lithosphere: Constraints imposed by laboratory experiments

The concept of strength envelopes, developed in the 1970s, allowed quantitative predictions of the strength of the lithosphere based on experimentally determined constitutive equations. Initial strength envelopes used an empirical relation for frictional sliding to describe deformation along brittle faults in the upper portion of the lithosphere and power law creep equations to estimate the plastic flow strength of rocks in the deeper part of the lithosphere. In the intervening decades, substantial progress has been made both in understanding the physical mechanisms involved in lithospheric deformation and in refining constitutive equations that describe these processes. The importance of a regime of semibrittle behavior is now recognized. Based on data from rocks without added pore fluids, the transition from brittle deformation to semibrittle flow can be estimated as the point at which the brittle fracture strength equals the peak stress to cause sliding. The transition from semibrittle deformation to plastic flow can be approximated as the stress at which the pressure exceeds the plastic flow strength. Current estimates of these stresses are on the order of a few hundred megapascals for relatively dry rocks. Knowledge of the stability of sliding along faults and of the onset of localization during brittle fracture has improved considerably. If the depth to the bottom of the seismogenic zone is determined by the transition to the stable frictional sliding regime, then that depth will be considerably more shallow than the depth of the transition to the plastic flow regime. Major questions concerning the strength of rocks remain. In particular, the effect of water on strength is critical to accurate predictions. Constitutive equations which include the effects of water fugacity and pore fluid pressure as well as temperature and strain rate are needed for both the brittle sliding and semibrittle flow regimes. Although the constitutive equations for dislocation creep and diffusional creep in single-phase aggregates are more robust, few data exist for plastic deformation in two-phase aggregates. Despite the fact that localization is ubiquitous in rocks deforming both in brittle and plastic regimes, only a limited amount of accurate experimental data are available to constrain predictions of this behavior. Accordingly, flow strengths now predicted from laboratory data probably overestimate the actual rock strength, perhaps by a significant amount. Still, the predictions are robust enough that uncertainties in geometry, mineralogy, loading conditions and thermodynamic state are probably the limiting factors in our understanding. Thus, experimentally determined rheologies can be applied to understand a broad range of topical problems in regional and global tectonics both on the Earth and on other planetary bodies.
Multiscale Fusion for Abnormality Detection and Localization of Distributed Parameter Systems

Numerous industrial thermal processes and fluid processes can be described by distributed parameter systems (DPSs), wherein many process parameters and variables vary in space and time. Early internal abnormalities in the DPS may develop into uncontrollable thermal failures, causing serious safety incidents. In this study, the multiscale information fusion is proposed for internal abnormality detection and localization of DPSs under different scenarios. We introduce the dissimilarity statistic as a means to identify anomalies for lumped variables, whereas spatial and temporal statistic measures are presented for the anomaly detection for distributed variables. Through appropriate parameter optimization, these statistic functions are integrated into the comprehensive multiscale detection index, which outperforms traditional single-scale detection methods. The proposed multiscale statistic has good physical interpretability from the system disorder degree. Experiments on the internal short circuit (ISC) of a battery system have demonstrated that our proposed method can swiftly identify ISC abnormalities and accurately pinpoint problematic battery cells under various working conditions.
Survey on Near-Space Information Networks: Channel Modeling, Networking, and Transmission Perspectives

Near-space information networks (NSIN) composed of high-altitude platforms (HAPs), high- and low-altitude unmanned aerial vehicles (UAVs) are a new regime for providing quick, robust, and cost-efficient sensing and communication services. Precipitated by innovations and breakthroughs in manufacturing, materials, communications, electronics, and control technologies, NSIN have emerged as an essential component of the emerging sixth-generation of mobile communication systems. This article aims at {presenting some critical issues and design proposals in our actual NSIN and} discussing the latest advances in NSIN in the research areas of channel modeling, networking, and transmission from a forward-looking, comparative, and technological evolutionary perspective. In this article, we highlight the characteristics of NSIN and present the promising use-cases of NSIN. The impact of airborne platforms' unstable movements on the phase delays of onboard antenna arrays with diverse structures is mathematically analyzed. The recent advancements in HAP channel modeling are elaborated on, along with the significant differences between HAP and UAV channel modeling. A comprehensive review of the networking technologies of NSIN in network deployment, handoff management, and network management aspects is provided. Besides, the promising technologies and communication protocols of the physical (PHY) layer, medium access control (MAC) layer, network layer, and transport layer of NSINs for achieving efficient transmission over NSINs are overviewed, {and we have conducted experiments with our actual NSIN to verify the performance of some techniques}. Finally, we outline some open issues and promising directions of NSINs deserved for future study and discuss the corresponding challenges.
Unified first law of thermodynamics in Gauss-Bonnet gravity on an FLRW background

Employing the thermodynamic unified first law through the thermodynamic-gravity conjecture, in this article, we derive for a FLRW universe the Friedmann equations in the framework of Gauss-Bonnet gravity theory. To do this, we project this generalized first law along the Kodama vector field and along the direction of an orthogonal vector to the Kodama vector. The second Friedmann equation is obtained by projecting on the Kodama vector, while the first is obtained by projecting along the flux on the Cauchy hypersurfaces. This result does not assume a priory temperature and an entropy, so the Clausius relation is not used here. Nevertheless, it is used to obtain the corresponding Gauss-Bonnet entropy. In this way, the validity of the generalized second law of thermodynamics is proved for the Gauss-Bonnet gravity theory.
Strategies for successful U‐Th dating of paleolake carbonates: An example from the Bolivian Altiplano

We report over 90 U‐Th dates from carbonates deposited around paleolakes on the Bolivian Altiplano. Petrographic and chemical data for tufas and the siliciclastic detritus contained within them allow (1) assessment of possible diagenetic effects, (2) a development of a strategy for selection of carbonate samples with low initial Th contents, and (3) assessment of the uncertainty due to initial Th. This strategy allows us to produce precise U‐Th dates from lacustrine carbonates. The principal consideration in dating such carbonates is the composition and quantity of initial Th incorporated into the carbonate, and additional uncertainty is introduced because this initial Th may have two sources in lacustrine deposits. Isochron plots, measured (230Th/232Th), and X‐ray diffraction and trace element chemistry of silicic residues all favor regional soil and dust as the sole source of initial Th in carbonates from the Bolivian Altiplano, a situation that simplifies single‐sample dating of Altiplano carbonates.
A unified framework for STAR-RIS coefficients optimization

Simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS), which serves users located on both sides of the surface, has recently emerged as a promising enhancement to the traditional reflective only RIS. Due to the lack of a unified comparison of communication systems equipped with different modes of STAR-RIS and the performance degradation caused by the constraints involving discrete selection, this paper proposes a unified optimization framework for handling the STAR-RIS operating mode and discrete phase constraints. With a judiciously introduced penalty term, this framework transforms the original problem into two iterative subproblems, with one containing the selection-type constraints, and the other subproblem handling other wireless resource. Convergent point of the whole algorithm is found to be at least a stationary point under mild conditions. As an illustrative example, the proposed framework is applied to a sum-rate maximization problem in the downlink transmission. Simulation results show that the algorithms from the proposed framework outperform other existing algorithms tailored for different STAR-RIS scenarios. Furthermore, it is found that 4 or even 2 discrete phases STAR-RIS could achieve almost the same sum-rate performance as the continuous phase setting, showing for the first time that discrete phase is not necessarily a cause of significant performance degradation.
Real-Time Event Detection with Random Forests and Temporal Convolutional Networks for More Sustainable Petroleum Industry

The petroleum industry is crucial for modern society, but the production process is complex and risky. During the production, accidents or failures, resulting from undesired production events, can cause severe environmental and economic damage. Previous studies have investigated machine learning (ML) methods for undesired event detection. However, the prediction of event probability in real-time was insufficiently addressed, which is essential since it is important to undertake early intervention when an event is expected to happen. This paper proposes two ML approaches, random forests and temporal convolutional networks, to detect undesired events in real-time. Results show that our approaches can effectively classify event types and predict the probability of their appearance, addressing the challenges uncovered in previous studies and providing a more effective solution for failure event management during the production.
Ar-40/Ar-39 geochronology of the Rajmahal basalts, India, and their relationship to the Kerguelen Plateau

During the mid-Cretaceous, extensive magmatism occurred in the Indian Ocean to form volcanic portions of the southern and central Kerguelen Plateau, Elan Bank and Broken Ridge. Basalt was erupted also along the rifted margin of eastern India (Rajmahal). We investigated the ages of these Indian basalts using Ar-40/Ar-39 incremental-heating experiments on whole rocks. Our results are consistent with the hypothesis that the lava pile of similar to230 m thickness in the Rajmahal Hills, Jharkhand, and alkalic basalts in the Bengal Basin were emplaced at similar to118 Ma. Dykes intruded to the SW of the Rajmahal Hills appear to be 2-3 Myr younger than these lavas. Magmatic activity in eastern India therefore was contemporaneous with the final stage of volcanism at Ocean Drilling Program Site 1136 on the Southern Kerguelen Plateau (119-118 Ma), but older than final magmatism at Sites 749 and 750 on the Southern Kerguelen Plateau (112-110 Ma), Site 1137 on Elan Bank (108 Ma) and Site 1138 on the Central Kerguelen Plateau (100 Ma). By combining these age data with plate reconstructions that take into account the motion of hotspots in a convecting mantle, we suggest that eruption of the Rajmahal basalts, formation of the Southern Kerguelen Plateau, and Elan Bank's separation from India are best explained by the presence of the Kerguelen hotspot close to the eastern Indian margin just after 120 Ma.