Miracle-dz/UniFAR-FTUs huggingface dataset:
Paleomagnetic and geochemical data from the late Miocene Lobato Formation adjacent to the Santa Clara fault system, Chili quadrangle, Rio Arriba County, New Mexico

New paleomagnetic and petrologic data from late Miocene volcanic rocks in the northern Espanola Basin of north-central New Mexico help constrain the late Tertiary tectonic history and landscape development of the area. We studied a 100-m-thick (328-ft-thick) section of the ~ 10 Ma Lobato Formation in Arroyo de la Plaza Larga, an east-trending drainage in the northeastern Jemez Mountains. The Lobato Formation represents some of the earliest precaldera mafic volcanism associated with the Jemez Mountain volcanic field and coincides with an episode of crustal extension in the Espanola Basin. At Arroyo de la Plaza Larga, Lobato Formation flows are subhorizontal for nearly 2 km (1.25 mi) southeast from their eruptive source in the Cerro Roman volcanic center. These flows take on a monoclinal geometry with an apparent northeast-trending fold axis where they flow over an erosional escarpment adjacent to the Santa Clara fault, a prominent northeast-striking structure along the western part of the Espanola Basin. Here we show that the apparent monocline is not of a structural origin but formed due to lava flow emplacement down an escarpment formed by displacement along the Santa Clara fault. One hundred thirty-four oriented samples were collected for paleomagnetic analysis from 16 sites from the hinge zone and east limb of the apparent monocline. Paleomagnetic data reveal a single-component magnetization that decays to the origin with less than 10% of the natural remanent magnetization remaining after treatment in 120 mT fields. In situ results from sites located in the hinge zone and those from the east fold limb yield statistically indistinguishable remanence directions. Following structural correction, based on the strike and dip of the individual flows, the dispersion between the two data sets increased, indicating failure of the fold test at the 95% confidence level. We argue that the Lobato Formation basalts from Cerro Roman were emplaced into a paleovalley of considerable relief adjacent to the Santa Clara fault and that during the late Miocene, it was an active structure that influenced the topography and drainage systems of the western margin of the Rio Grande rift.
DRAUC: An Instance-wise Distributionally Robust AUC Optimization Framework

The Area Under the ROC Curve (AUC) is a widely employed metric in long-tailed classification scenarios. Nevertheless, most existing methods primarily assume that training and testing examples are drawn i.i. d. from the same distribution, which is often unachievable in practice. Distributionally Robust Optimization (DRO) enhances model performance by optimizing it for the local worst-case scenario, but directly integrating AUC optimization with DRO results in an intractable optimization problem. To tackle this challenge, methodically we propose an instance-wise surrogate loss of Distributionally Robust AUC (DRAUC) and build our optimization framework on top of it. Moreover, we highlight that conventional DRAUC may induce label bias, hence introducing distribution-aware DRAUC as a more suitable metric for robust AUC learning. Theoretically, we affirm that the generalization gap between the training loss and testing error diminishes if the training set is sufficiently large. Empirically, experiments on corrupted benchmark datasets demonstrate the effectiveness of our proposed method. Code is available at: https://github.com/EldercatSAM/DRAUC.
Improved Convergence Rates of Windowed Anderson Acceleration for Symmetric Fixed-Point Iterations

This paper studies the commonly utilized windowed Anderson acceleration (AA) algorithm for fixed-point methods, $x^{(k+1)}=q(x^{(k)})$. It provides the first proof that when the operator $q$ is linear and symmetric the windowed AA, which uses a sliding window of prior iterates, improves the root-linear convergence factor over the fixed-point iterations. When $q$ is nonlinear, yet has a symmetric Jacobian at a fixed point, a slightly modified AA algorithm is proved to have an analogous root-linear convergence factor improvement over fixed-point iterations. Simulations verify our observations. Furthermore, experiments with different data models demonstrate AA is significantly superior to the standard fixed-point methods for Tyler's M-estimation.
Spectrum of the Laplacian on the Fricke-Macbeath surface

The Fricke-Macbeath surface is the unique Hurwitz surface of genus 7 with 504 conformal automorphisms. In this paper, we prove that the first eigenvalue of the Laplacian on the Fricke-Macbeath surface has a sevenfold multiplicity and contained in the interval $[1.23, 1.26]$. Further, we numerically identify the 7-dimensional representation of its automorphism group corresponding to the eigenspace associated to the first eigenvalue. We also determine the Dirichlet domain centered at 0 for a Fuchsian group that uniformizes the Fricke-Macbeath surface, identifying the algebraic coordinates for its vertices.
Learning Time-Invariant Representations for Individual Neurons from Population Dynamics

Neurons can display highly variable dynamics. While such variability presumably supports the wide range of behaviors generated by the organism, their gene expressions are relatively stable in the adult brain. This suggests that neuronal activity is a combination of its time-invariant identity and the inputs the neuron receives from the rest of the circuit. Here, we propose a self-supervised learning based method to assign time-invariant representations to individual neurons based on permutation-, and population size-invariant summary of population recordings. We fit dynamical models to neuronal activity to learn a representation by considering the activity of both the individual and the neighboring population. Our self-supervised approach and use of implicit representations enable robust inference against imperfections such as partial overlap of neurons across sessions, trial-to-trial variability, and limited availability of molecular (transcriptomic) labels for downstream supervised tasks. We demonstrate our method on a public multimodal dataset of mouse cortical neuronal activity and transcriptomic labels. We report>35% improvement in predicting the transcriptomic subclass identity and>20% improvement in predicting class identity with respect to the state-of-the-art.
Approximating Langevin Monte Carlo with ResNet-like Neural Network architectures

We sample from a given target distribution by constructing a neural network which maps samples from a simple reference, e.g. the standard normal distribution, to samples from the target. To that end, we propose using a neural network architecture inspired by the Langevin Monte Carlo (LMC) algorithm. Based on LMC perturbation results, we show approximation rates of the proposed architecture for smooth, log-concave target distributions measured in the Wasserstein-$2$ distance. The analysis heavily relies on the notion of sub-Gaussianity of the intermediate measures of the perturbed LMC process. In particular, we derive bounds on the growth of the intermediate variance proxies under different assumptions on the perturbations. Moreover, we propose an architecture similar to deep residual neural networks and derive expressivity results for approximating the sample to target distribution map.
Status of demersal fishery resources of Malaysia

Research trawl surveys have been conducted in four areas of Malaysian waters (west and east coast of Peninsular Malaysia, and waters off the coast of Sarawak and off the west coast of Sabah) since 1970. Selected surveys (1972 - 98) were used to examine the status of demersal fishery resources in each area, focusing on catch rate and biomass trends, population parameters and the exploitation rates of dominant species. Annual fishery catch and effort data for Peninsular Malaysia (1971 - 96) and Sarawak (1969 - 96) were used to assess the maximum sustainable yield (MSY) and fishing effort to achieve optimum level (fMSY). The results indicate over-exploitation of the demersal resources, severe depletion and excess effort in the fisheries. The catch rates have declined up to 96% in some regions, while biomass estimates are down to 6% of the virgin stock biomass. Most of the dominant species have exploitation ratios over 0.40. Analysis of the standardized fishing effort and yield using the Fox model indicates that the 1996 effort is 135 - 200% of the level needed to harvest MSY. The levels of reduction of catch and of exploitation vary among the four areas and with depth, but most show over-exploitation and severe reductions.
HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification

The human brain can be considered as complex networks, composed of various regions that continuously exchange their information with each other, forming the brain network graph, from which nodes and edges are extracted using resting-state functional magnetic resonance imaging (rs-fMRI). Therefore, this graph can potentially depict abnormal patterns that have emerged under the influence of brain disorders. So far, numerous studies have attempted to find embeddings for brain network graphs and subsequently classify samples with brain disorders from healthy ones, which include limitations such as: not considering the relationship between samples, not utilizing phenotype information, lack of temporal analysis, using static functional connectivity (FC) instead of dynamic ones and using a fixed graph structure. We propose a hierarchical dynamic graph representation learning (HDGL) model, which is the first model designed to address all the aforementioned challenges. HDGL consists of two levels, where at the first level, it constructs brain network graphs and learns their spatial and temporal embeddings, and at the second level, it forms population graphs and performs classification after embedding learning. Furthermore, based on how these two levels are trained, four methods have been introduced, some of which are suggested for reducing memory complexity. We evaluated the performance of the proposed model on the ABIDE and ADHD-200 datasets, and the results indicate the improvement of this model compared to several state-of-the-art models in terms of various evaluation metrics.
AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction Estimation

One of the main challenges in offline Reinforcement Learning (RL) is the distribution shift that arises from the learned policy deviating from the data collection policy. This is often addressed by avoiding out-of-distribution (OOD) actions during policy improvement as their presence can lead to substantial performance degradation. This challenge is amplified in the offline Multi-Agent RL (MARL) setting since the joint action space grows exponentially with the number of agents. To avoid this curse of dimensionality, existing MARL methods adopt either value decomposition methods or fully decentralized training of individual agents. However, even when combined with standard conservatism principles, these methods can still result in the selection of OOD joint actions in offline MARL. To this end, we introduce AlberDICE, an offline MARL algorithm that alternatively performs centralized training of individual agents based on stationary distribution optimization. AlberDICE circumvents the exponential complexity of MARL by computing the best response of one agent at a time while effectively avoiding OOD joint action selection. Theoretically, we show that the alternating optimization procedure converges to Nash policies. In the experiments, we demonstrate that AlberDICE significantly outperforms baseline algorithms on a standard suite of MARL benchmarks.
On the Second-Order Convergence of Biased Policy Gradient Algorithms

Since the objective functions of reinforcement learning problems are typically highly nonconvex, it is desirable that policy gradient, the most popular algorithm, escapes saddle points and arrives at second-order stationary points. Existing results only consider vanilla policy gradient algorithms with unbiased gradient estimators, but practical implementations under the infinite-horizon discounted reward setting are biased due to finite-horizon sampling. Moreover, actor-critic methods, whose second-order convergence has not yet been established, are also biased due to the critic approximation of the value function. We provide a novel second-order analysis of biased policy gradient methods, including the vanilla gradient estimator computed from Monte-Carlo sampling of trajectories as well as the double-loop actor-critic algorithm, where in the inner loop the critic improves the approximation of the value function via TD(0) learning. Separately, we also establish the convergence of TD(0) on Markov chains irrespective of initial state distribution.
Relativistic electron precipitation events driven by solar wind impact on the Earth's magnetosphere

Certain forms of solar wind transients contain significant enhancements of dynamic pressure and may effectively drive magnetosphere dynamics, including substorms and storms. An integral element of such driving is the generation of a wide range of electromagnetic waves within the inner magnetosphere, either by compressionally heated plasma or by substorm plasma sheet injections. Consequently, solar wind transient impacts are traditionally associated with energetic electron scattering and losses into the atmosphere by electromagnetic waves. In this study, we show the first direct measurements of two such transient-driven precipitation events as measured by the low-altitude Electron Losses and Fields Investigation (ELFIN) CubeSats. The first event demonstrates storm-time generated electromagnetic ion cyclotron waves efficiently precipitating relativistic electrons from>300 keV to 2 MeV at the duskside. The second event demonstrates whistler-mode waves leading to scattering of electrons from 50 keV to 700 keV on the dawnside. These observations confirm the importance of solar wind transients in driving energetic electron losses and subsequent dynamics in the ionosphere.
Revivals in time-evolution quasi-periodic problems

We examine the influence of quasi-periodic boundary conditions on the phenomenon of revivals in linear dispersive PDEs. We show that, in general, quasi-periodic problems do not support the revival effect at rational times. Our method is based on a correspondence between quasi-periodic and periodic problems. We prove that the solution to a quasi-periodic problem is expressed via the solution to a corresponding periodic problem, and vice-versa. Then, our main results follow by deriving a representation of the periodic problem solution in terms of a composition of solutions for a particular class of periodic problems, where the latter supports the classical revival and fractalisation dichotomy at rational and irrational times.
VR-NeRF: High-Fidelity Virtualized Walkable Spaces

We present an end-to-end system for the high-fidelity capture, model reconstruction, and real-time rendering of walkable spaces in virtual reality using neural radiance fields. To this end, we designed and built a custom multi-camera rig to densely capture walkable spaces in high fidelity and with multi-view high dynamic range images in unprecedented quality and density. We extend instant neural graphics primitives with a novel perceptual color space for learning accurate HDR appearance, and an efficient mip-mapping mechanism for level-of-detail rendering with anti-aliasing, while carefully optimizing the trade-off between quality and speed. Our multi-GPU renderer enables high-fidelity volume rendering of our neural radiance field model at the full VR resolution of dual 2K × 2K at 36 Hz on our custom demo machine. We demonstrate the quality of our results on our challenging high-fidelity datasets, and compare our method and datasets to existing baselines. We release our dataset on our project website: https://vr-nerf.github. io.
Sample Complexity of Opinion Formation on Networks

Consider public health officials aiming to spread awareness about a new vaccine in a community interconnected by a social network. How can they distribute information with minimal resources, ensuring community-wide understanding that aligns with the actual facts? This concern mirrors numerous real-world situations. In this paper, we initialize the study of sample complexity in opinion formation to solve this problem. Our model is built on the recognized opinion formation game, where we regard each agent's opinion as a data-derived model parameter, not just a real number as in prior studies. Such an extension offers a wider understanding of opinion formation and ties closely with federated learning. Through this formulation, we characterize the sample complexity bounds for any network and also show asymptotically tight bounds for specific network structures. Intriguingly, we discover optimal strategies often allocate samples inversely to the degree, hinting at vital policy implications. Our findings are empirically validated on both synthesized and real-world networks.
Enhanced Sensitivity of THz NbN Hot Electron Bolometer Mixers

We studied the effect of the NbN/Au contact on the sensitivities of a NbN hot electron bolometer (HEB) mixer by measuring the double sideband (DSB) receiver noise temperature (T_rec_DSB) at three local oscillator frequencies of 1.6, 2.5 and 5.3 THz. The HEB has cleaned contact structures with a thick Au layer. We demonstrated low mixer noise temperatures (T_mixer_DSB) of 240 K and 290 K at 1.6 and 2.5 THz, respectively. The latter reach roughly 3 times the quantum noise at their frequencies. The mixer is developed for the proposed OASIS and SALTUS (concept) missions. The enhanced T_mixer_DSB are more than 30 % better in comparison with published NbN HEB mixers. The improvement can reduce the integration time of a heterodyne instrument roughly by a factor of 2. The T_mixer^DSB of the same HEB has shown limited improvement at 5.3 THz, which is partly due to non-optimized antenna geometry. Besides, the results also help to understand device physics of a wide HEB (4 um) at high frequencies.
Online Long-run Constrained Optimization

In this paper, a novel Follow-the-Perturbed-Leader type algorithm is proposed and analyzed for solving general long-term constrained optimization problems in online manner, where the objective and constraints are not necessarily convex. In each period, random linear perturbation and strongly concave perturbation are incorporated in primal and dual directions, respectively, to the offline oracle, and a global minimax point is searched as solution. Based on two particular definitions of expected static cumulative regret, we derive the first sublinear $O(T^{8/9})$ regret complexity for this class of problems. The proposed algorithm is applied to tackle a long-term (risk) constrained river pollutant source identification problem, demonstrating the validity of the theoretical results and exhibiting superior performance compared to existing method.
Analysis of the sparse super resolution limit using the Cramér-Rao lower bound

Already since the work by Abbe and Rayleigh the difficulty of super resolution where one wants to recover a collection of point sources from low-resolved microscopy measurements is thought to be dependent on whether the distance between the sources is below or above a certain resolution or diffraction limit. Even though there has been a number of approaches to define this limit more rigorously, there is still a gap between the situation where the task is known to be hard and scenarios where the task is provably simpler. For instance, an interesting approach for the univariate case using the size of the Cram\'er-Rao lower bound was introduced in a recent work by Ferreira Da Costa and Mitra. In this paper, we prove their conjecture on the transition point between good and worse tractability of super resolution and extend it to higher dimensions. Specifically, the bivariate statistical analysis allows to link the findings by the Cram\'er-Rao lower bound to the classical Rayleigh limit.
Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection

Large Multimodal Model (LMM) GPT-4V(ision) endows GPT-4 with visual grounding capabilities, making it possible to handle certain tasks through the Visual Question Answering (VQA) paradigm. This paper explores the potential of VQA-oriented GPT-4V in the recently popular visual Anomaly Detection (AD) and is the first to conduct qualitative and quantitative evaluations on the popular MVTec AD and VisA datasets. Considering that this task requires both image-/pixel-level evaluations, the proposed GPT-4V-AD framework contains three components: 1) Granular Region Division, 2) Prompt Designing, 3) Text2Segmentation for easy quantitative evaluation, and have made some different attempts for comparative analysis. The results show that GPT-4V can achieve certain results in the zero-shot AD task through a VQA paradigm, such as achieving image-level 77.1/88.0 and pixel-level 68.0/76.6 AU-ROCs on MVTec AD and VisA datasets, respectively. However, its performance still has a certain gap compared to the state-of-the-art zero-shot method, e.g., WinCLIP ann CLIP-AD, and further research is needed. This study provides a baseline reference for the research of VQA-oriented LMM in the zero-shot AD task, and we also post several possible future works. Code is available at \url{https://github.com/zhangzjn/GPT-4V-AD}.
On optimal control of reflected diffusions

We study a simple singular control problem for a Brownian motion with constant drift and variance reflected at the origin. Exerting control pushes the process towards the origin and generates a concave increasing state-dependent yield which is discounted at a fixed rate. The most interesting feature of the problem is that its solution can be more complicated than anticipated. Indeed, for some parameter values, the optimal policy involves two reflecting barriers and one repelling boundary, the action region being the union of two disjoint intervals. We also show that the apparent anomaly can be understood as involving a switch between two strategies with different risk profiles: The risk-neutral decision maker initially gambles on the more risky strategy, but lowers risk if this strategy underperforms.
Three-dimensional checkerboard spin structure on a breathing pyrochlore lattice

The standard approach to realize a spin liquid state is through magnetically frustrated states, relying on ingredients such as the lattice geometry, dimensionality, and magnetic interaction type of the spins. While Heisenberg spins on a pyrochlore lattice with only antiferromagnetic nearest neighbors interactions are theoretically proven disordered, spins in real systems generally include longer-range interactions. The spatial correlations at longer distances typically stabilize a long-range order rather than enhancing a spin liquid state. Both states can, however, be destroyed by short-range static correlations introduced by chemical disorder. Here, using disorder-free specimens with a clear long-range antiferromagnetic order, we refine the spin structure of the Heisenberg spinel ZnFe2O4 through neutron magnetic diffraction. The unique wave vector (1, 0, 1/2) leads to a spin structure that can be viewed as alternatively stacked ferromagnetic and antiferromagnetic tetrahedra in a three-dimensional checkerboard form. Stable coexistence of these opposing types of clusters is enabled by the bipartite breathing-pyrochlore crystal structure, leading to a second order phase transition at 10 K. The diffraction intensity of ZnFe2O4 is an exact complement to the inelastic scattering intensity of several chromate spinel systems which are regarded as model classical spin liquids. Our results challenge this attribution, and suggest instead of the six-spin ring-mode, spin excitations in chromate spinels are closely related to the (1, 0, 1/2) type of spin order and the four-spin ferromagnetic cluster locally at one tetrahedron.