Miracle-dz/UniFAR-FTUs huggingface dataset:
FORGE’d in FIRE II: The Formation of Magnetically-Dominated Quasar Accretion Disks from Cosmological Initial Conditions

In a companion paper, we reported the self-consistent formation of quasar accretion disks with inflow rates $\sim 10\,{\rm M_{\odot}\,yr^{-1}}$ down to<300 Schwarzschild radii from cosmological radiation-magneto-thermochemical-hydrodynamical galaxy and star formation simulations. We see the formation of a well-defined, steady-state accretion disk which is stable against star formation at sub-pc scales. The disks are optically thick, with radiative cooling balancing accretion, but with properties that are distinct from those assumed in most previous accretion disk models. The pressure is strongly dominated by (primarily toroidal) magnetic fields, with a plasma $\beta \sim 10^{-4}$ even in the disk midplane. They are qualitatively distinct from magnetically elevated or arrested disks. The disks are strongly turbulent, with trans-Alfvenic and highly super-sonic turbulence, and balance this via a cooling time that is short compared to the disk dynamical time, and can sustain highly super-Eddington accretion rates. Their surface and 3D densities at $\sim 10^{3}-10^{5}$ gravitational radii are much lower than in a Shakura-Sunyaev disk, with important implications for their thermo-chemistry and stability. We show how the magnetic field strengths and geometries arise from rapid advection of flux with the inflow from much weaker galaxy-scale fields in these 'flux-frozen' disks, and how this stabilizes the disk and gives rise to efficient torques. Re-simulating without magnetic fields produces catastrophic fragmentation with a vastly smaller, lower-$\dot{M}$ Shakura-Sunyaev-like disk.
Stratigraphy of the Pliocene Sørsdal Formation, Marine Plain, Vestfold Hills, East Antarctica

The Sørsdal Formation and one member, Graveyard Sandstone Member constitute a sedimentary sequence covering approximately 10 km2 of Marine Plain, Vestfold Hills, East Antarctica. The new Formation consists dominantly of friable diatomaceous siltstone and sandstone with dark limestone lenses. It is in situ, essentially horizontal, 7.2 m thick in its type section and lies less than 25 m a.s. l. Graveyard Sandstone Member occurs near the top of the formation, is highly lithified sandy diamictite, 30–50 cm thick and widespread through the Marine Plain region. Using diatoms, the Formation is Early Pliocene in age (Fragilariopsis barronii, 4.5–4.1Ma). The Graveyard Sandstone Member probably was deposited during the Gilbert Chron interval (lower Chron 2Ar or C3n. 1r) of reversed magnetic polarity. The Sørsdal Formation contains fossil cetaceans and a diverse and well-preserved invertebrate fauna. Foraminifera are rare partly because of diagenesis, but include Ammoelphidiella antarctica. No evidence of coeval terrestrial vegetation has been recovered. The deposit accumulated in a series of small bays probably in an environment warmer than exists in the region today. There is no lithological evidence of glacial influence except in Graveyard Sandstone Member that may represent local glacial influence in a shallow marine to intertidal environment.
Boundary rigidity of finite CAT(0) cube complexes

In this note, we prove that finite CAT(0) cube complexes can be reconstructed from their boundary distances (computed in their 1-skeleta). This result was conjectured by Haslegrave, Scott, Tamitegama, and Tan (2023). The reconstruction of a finite cell complex from the boundary distances is the discrete version of the boundary rigidity problem, which is a classical problem from Riemannian geometry. In the proofs, we use the bijection between CAT(0) cube complexes and median graphs and the corner peelings of median graphs.
Toward More Accurate and Robust Optimal Power Flow for Distribution Systems

The objective of this paper is to improve the accuracy and robustness of optimal power flow (OPF) formulations for distribution systems modeled down to the low-voltage point of connection of individual buildings. An approach for addressing the uncertain switching behavior of building loads(e.g., air conditioners, water heaters, or pool pumps) and variable renewable generation (e.g., rooftop solar) in the OPF is proposed. Rather than using time-averaged forecasts to determine voltage magnitude constraints, we leverage worst-case minimum and maximum forecasts of loads and distributed energy resource generation. Sensitivities of the power flow equations are used to predict how these deviations in load and renewable generation will impact system voltages, and the voltage constraints in the OPF are dynamically adjusted to mitigate voltage violations due to this uncertainty. A methodology for incorporating models of split-phase components and transformer core losses in the OPF formulation is also proposed. The proposed approach is validated through numerical case studies on a realistic distribution feeder using GridLAB-D, a distribution system simulation software.
Better and Simpler Lower Bounds for Differentially Private Statistical Estimation

We provide optimal lower bounds for two well-known parameter estimation (also known as statistical estimation) tasks in high dimensions with approximate differential privacy. First, we prove that for any $\alpha \le O(1)$, estimating the covariance of a Gaussian up to spectral error $\alpha$ requires $\tilde{\Omega}\left(\frac{d^{3/2}}{\alpha \varepsilon} + \frac{d}{\alpha^2}\right)$ samples, which is tight up to logarithmic factors. This result improves over previous work which established this for $\alpha \le O\left(\frac{1}{\sqrt{d}}\right)$, and is also simpler than previous work. Next, we prove that estimating the mean of a heavy-tailed distribution with bounded $k$th moments requires $\tilde{\Omega}\left(\frac{d}{\alpha^{k/(k-1)} \varepsilon} + \frac{d}{\alpha^2}\right)$ samples. Previous work for this problem was only able to establish this lower bound against pure differential privacy, or in the special case of $k = 2$. Our techniques follow the method of fingerprinting and are generally quite simple. Our lower bound for heavy-tailed estimation is based on a black-box reduction from privately estimating identity-covariance Gaussians. Our lower bound for covariance estimation utilizes a Bayesian approach to show that, under an Inverse Wishart prior distribution for the covariance matrix, no private estimator can be accurate even in expectation, without sufficiently many samples.
LAiW: A Chinese Legal Large Language Models Benchmark (A Technical Report)

General and legal domain LLMs have demonstrated strong performance in various tasks of LegalAI. However, the current evaluations of these LLMs in LegalAI are defined by the experts of computer science, lacking consistency with the logic of legal practice, making it difficult to judge their practical capabilities. To address this challenge, we are the first to build the Chinese legal LLMs benchmark LAiW, based on the logic of legal practice. To align with the thinking process of legal experts and legal practice (syllogism), we divide the legal capabilities of LLMs from easy to difficult into three levels: basic information retrieval, legal foundation inference, and complex legal application. Each level contains multiple tasks to ensure a comprehensive evaluation. Through automated evaluation of current general and legal domain LLMs on our benchmark, we indicate that these LLMs may not align with the logic of legal practice. LLMs seem to be able to directly acquire complex legal application capabilities but perform poorly in some basic tasks, which may pose obstacles to their practical application and acceptance by legal experts. To further confirm the complex legal application capabilities of current LLMs in legal application scenarios, we also incorporate human evaluation with legal experts. The results indicate that while LLMs may demonstrate strong performance, they still require reinforcement of legal logic.
Comparative Analysis of Transfer Learning in Deep Learning Text-to-Speech Models on a Few-Shot, Low-Resource, Customized Dataset

Text-to-Speech (TTS) synthesis using deep learning relies on voice quality. Modern TTS models are advanced, but they need large amount of data. Given the growing computational complexity of these models and the scarcity of large, high-quality datasets, this research focuses on transfer learning, especially on few-shot, low-resource, and customized datasets. In this research,"low-resource"specifically refers to situations where there are limited amounts of training data, such as a small number of audio recordings and corresponding transcriptions for a particular language or dialect. This thesis, is rooted in the pressing need to find TTS models that require less training time, fewer data samples, yet yield high-quality voice output. The research evaluates TTS state-of-the-art model transfer learning capabilities through a thorough technical analysis. It then conducts a hands-on experimental analysis to compare models' performance in a constrained dataset. This study investigates the efficacy of modern TTS systems with transfer learning on specialized datasets and a model that balances training efficiency and synthesis quality. Initial hypotheses suggest that transfer learning could significantly improve TTS models' performance on compact datasets, and an optimal model may exist for such unique conditions. This thesis predicts a rise in transfer learning in TTS as data scarcity increases. In the future, custom TTS applications will favour models optimized for specific datasets over generic, data-intensive ones.
Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning

We study matrix estimation problems arising in reinforcement learning (RL) with low-rank structure. In low-rank bandits, the matrix to be recovered specifies the expected arm rewards, and for low-rank Markov Decision Processes (MDPs), it may for example characterize the transition kernel of the MDP. In both cases, each entry of the matrix carries important information, and we seek estimation methods with low entry-wise error. Importantly, these methods further need to accommodate for inherent correlations in the available data (e.g. for MDPs, the data consists of system trajectories). We investigate the performance of simple spectral-based matrix estimation approaches: we show that they efficiently recover the singular subspaces of the matrix and exhibit nearly-minimal entry-wise error. These new results on low-rank matrix estimation make it possible to devise reinforcement learning algorithms that fully exploit the underlying low-rank structure. We provide two examples of such algorithms: a regret minimization algorithm for low-rank bandit problems, and a best policy identification algorithm for reward-free RL in low-rank MDPs. Both algorithms yield state-of-the-art performance guarantees.
On the Error-Propagation of Inexact Deflation for Principal Component Analysis

Principal Component Analysis (PCA) is a popular tool in data analysis, especially when the data is high-dimensional. PCA aims to find subspaces, spanned by the so-called \textit{principal components}, that best explain the variance in the dataset. The deflation method is a popular meta-algorithm -- used to discover such subspaces -- that sequentially finds individual principal components, starting from the most important one and working its way towards the less important ones. However, due to its sequential nature, the numerical error introduced by not estimating principal components exactly -- e.g., due to numerical approximations through this process -- propagates, as deflation proceeds. To the best of our knowledge, this is the first work that mathematically characterizes the error propagation of the inexact deflation method, and this is the key contribution of this paper. We provide two main results: $i)$ when the sub-routine for finding the leading eigenvector is generic, and $ii)$ when power iteration is used as the sub-routine. In the latter case, the additional directional information from power iteration allows us to obtain a tighter error bound than the analysis of the sub-routine agnostic case. As an outcome, we provide explicit characterization on how the error progresses and affects subsequent principal component estimations for this fundamental problem.
Characterization of principal bundles: the commutative case

A review of the characterization of principal bundles, through the different properties of the action of a group and its related canonical and translation maps, is presented. The work is divided in three stages: a topological group acting on a topological space, a discrete group acting on a smooth manifold, and a Lie group acting on a smooth manifold.
Joint inversion of Time-Lapse Surface Gravity and Seismic Data for Monitoring of 3D CO2 Plumes via Deep Learning

We introduce a fully 3D, deep learning-based approach for the joint inversion of time-lapse surface gravity and seismic data for reconstructing subsurface density and velocity models. The target application of this proposed inversion approach is the prediction of subsurface CO2 plumes as a complementary tool for monitoring CO2 sequestration deployments. Our joint inversion technique outperforms deep learning-based gravity-only and seismic-only inversion models, achieving improved density and velocity reconstruction, accurate segmentation, and higher R-squared coefficients. These results indicate that deep learning-based joint inversion is an effective tool for CO$_2$ storage monitoring. Future work will focus on validating our approach with larger datasets, simulations with other geological storage sites, and ultimately field data.
Invariants of real vector bundles

For a compact smooth manifold with corners (or finite CW-complex) $X$, we can prescribe a finite set of spin or spin$^h$ manifolds (possibly with boundary) mapping into it so that every real vector bundle over $X$ is determined, up to stable equivalence, by the Dirac indices of the real vector bundle when pulled-back onto those prescribed spin or spin$^h$ manifolds.
Colmap-PCD: An Open-source Tool for Fine Image-to-point cloud Registration

State-of-the-art techniques for monocular camera reconstruction predominantly rely on the Structure from Motion (SfM) pipeline. However, such methods often yield reconstruction outcomes that lack crucial scale information, and over time, accumulation of images leads to inevitable drift issues. In contrast, mapping methods based on LiDAR scans are popular in large-scale urban scene reconstruction due to their precise distance measurements, a capability fundamentally absent in visual-based approaches. Researchers have made attempts to utilize concurrent LiDAR and camera measurements in pursuit of precise scaling and color details within mapping outcomes. However, the outcomes are subject to extrinsic calibration and time synchronization precision. In this paper, we propose a novel cost-effective reconstruction pipeline that utilizes a pre-established LiDAR map as a fixed constraint to effectively address the inherent scale challenges present in monocular camera reconstruction. To our knowledge, our method is the first to register images onto the point cloud map without requiring synchronous capture of camera and LiDAR data, granting us the flexibility to manage reconstruction detail levels across various areas of interest. To facilitate further research in this domain, we have released Colmap-PCD${^{3}}$, an open-source tool leveraging the Colmap algorithm, that enables precise fine-scale registration of images to the point cloud map.
Data efficient deep learning for medical image analysis: A survey

The rapid evolution of deep learning has significantly advanced the field of medical image analysis. However, despite these achievements, the further enhancement of deep learning models for medical image analysis faces a significant challenge due to the scarcity of large, well-annotated datasets. To address this issue, recent years have witnessed a growing emphasis on the development of data-efficient deep learning methods. This paper conducts a thorough review of data-efficient deep learning methods for medical image analysis. To this end, we categorize these methods based on the level of supervision they rely on, encompassing categories such as no supervision, inexact supervision, incomplete supervision, inaccurate supervision, and only limited supervision. We further divide these categories into finer subcategories. For example, we categorize inexact supervision into multiple instance learning and learning with weak annotations. Similarly, we categorize incomplete supervision into semi-supervised learning, active learning, and domain-adaptive learning and so on. Furthermore, we systematically summarize commonly used datasets for data efficient deep learning in medical image analysis and investigate future research directions to conclude this survey.
Empirical Exploration of Deformation Mechanisms in Deep Rock Exposed to Multiple Stresses

To avoid the impact of inherent natural imperfections on experimental outcomes during testing, a recently designed genuine triaxial apparatus has enabled the replication of conditions where the three principal stresses exhibit varying magnitudes. This setup facilitates the examination of fracture behaviors in materials similar to the 4-series under genuine triaxial stress states. The experimental findings revealed that the maximum strength experiences significant influence from the minimum principal stress and the intermediate principal stress in genuine triaxial stress conditions. Specifically, for a fixed minimum principal stress ({\sigma}3), there is a progressive reduction in peak strain as the intermediate principal stress ({\sigma}2) increases. Moreover, as the minimum principal stress ({\sigma}3) rises, the magnitude of peak strain diminishes. The Young's modulus of the specimens exhibits an exponential increase with the augmentation of confining pressure, and it demonstrates a linear growth pattern as the intermediate principal stress increases. Additionally, the cohesion force is observed to rise while the internal friction angle decreases with the elevation of the intermediate principal stress. The failure modes of the specimens in genuine triaxial stress conditions can be categorized into three types, all of which exhibit a specific angle between the fracture surface and {\sigma}3, ranging from 50 to 62 degrees. Furthermore, a comparison between the computed range and the actual measurement range of the fracture angle highlights several limitations in the relationship between the fracture angle ({\theta}) and the internal friction angle ({\phi}) within the Coulomb criterion, particularly in rocks containing complex fracture systems.
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning

Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. Sharing clients' information has shown great potentiality in mitigating data heterogeneity, yet incurs a dilemma in preserving privacy and promoting model performance. To alleviate the dilemma, we raise a fundamental question: \textit{Is it possible to share partial features in the data to tackle data heterogeneity? } In this work, we give an affirmative answer to this question by proposing a novel approach called {\textbf{Fed}erated \textbf{Fe}ature \textbf{d}istillation} (FedFed). Specifically, FedFed partitions data into performance-sensitive features (i.e., greatly contributing to model performance) and performance-robust features (i.e., limitedly contributing to model performance). The performance-sensitive features are globally shared to mitigate data heterogeneity, while the performance-robust features are kept locally. FedFed enables clients to train models over local and shared data. Comprehensive experiments demonstrate the efficacy of FedFed in promoting model performance.
From Data to Dialogue: Leveraging the Structure of Knowledge Graphs for Conversational Exploratory Search

Exploratory search is an open-ended information retrieval process that aims at discovering knowledge about a topic or domain rather than searching for a specific answer or piece of information. Conversational interfaces are particularly suitable for supporting exploratory search, allowing users to refine queries and examine search results through interactive dialogues. In addition to conversational search interfaces, knowledge graphs are also useful in supporting information exploration due to their rich semantic representation of data items. In this study, we demonstrate the synergistic effects of combining knowledge graphs and conversational interfaces for exploratory search, bridging the gap between structured and unstructured information retrieval. To this end, we propose a knowledge-driven dialogue system for exploring news articles by asking natural language questions and using the graph structure to navigate between related topics. Based on a user study with 54 participants, we empirically evaluate the effectiveness of the graph-based exploratory search and discuss design implications for developing such systems.
Tectonic setting of Cretaceous basins on the NE Tibetan Plateau: insights from the Jungong basin

Quantifying the Cenozoic growth of high topography in the Indo‐Asian collision zone remains challenging, due in part to significant shortening that occurred within Eurasia before collision. A growing body of evidence suggests that regions far removed from the suture zone experienced deformation before and during the early phases of Himalayan orogenesis. In the present‐day north‐eastern Tibetan Plateau, widespread deposits of Cretaceous sediment attest to significant basin formation; however, the tectonic setting of these basins remains enigmatic. We present a study of a regionally extensive network of sedimentary basins that are spatially associated with a system of SE‐vergent thrust faults and are now exposed in the high ranges of the north‐eastern corner of the Tibetan Plateau. We focus on a particularly well‐exposed basin, located ∼20 km north of the Kunlun fault in the Anyemaqen Shan. The basin is filled by ∼900 m of alluvial sediments that become finer‐grained away from the basin‐bounding fault. Additionally, beds in the proximal footwall of the basin‐bounding fault exhibit progressive, up‐section shallowing and several intraformational unconformities which can be traced into correlative conformities in the distal part of the basin. The observations show sediment accumulated in the basin during fault motion. Regional constraints on the timing of sediment deposition are provided by both fossil assemblages from the Early Cretaceous, and by K–Ar dating of volcanic rocks that floor and cross‐cut sedimentary fill. We argue that during the Cretaceous, the interior NE Tibetan Plateau experienced NW–SE contractional deformation similar to that documented throughout the Qinling–Dabie orogen to the east. The Songpan‐Ganzi terrane apparently marked the southern limit of this deformation, such that it may have been a relatively rigid block in the Tibetan lithosphere, separating regions experiencing deformation north of the convergent Tethyan margin from regions deforming inboard of the east Asian margin.