Miracle-dz/UniFAR-FTUs huggingface dataset:
Diffusion Probabilistic Fields

Diffusion probabilistic models have quickly become a major approach for generative modeling of images, 3D geometry, video and other domains. However, to adapt diffusion generative modeling to these domains the denoising network needs to be carefully designed for each domain independently, oftentimes under the assumption that data lives in a Euclidean grid. In this paper we introduce Diffusion Probabilistic Fields (DPF), a diffusion model that can learn distributions over continuous functions defined over metric spaces, commonly known as fields. We extend the formulation of diffusion probabilistic models to deal with this field parametrization in an explicit way, enabling us to define an end-to-end learning algorithm that side-steps the requirement of representing fields with latent vectors as in previous approaches (Dupont et al., 2022a; Du et al., 2021). We empirically show that, while using the same denoising network, DPF effectively deals with different modalities like 2D images and 3D geometry, in addition to modeling distributions over fields defined on non-Euclidean metric spaces.
Effective chaos for the Kirchhoff equation on tori

We consider the Kirchhoff equation on tori of any dimension and we construct solutions whose Sobolev norms oscillates in a chaotic way on certain long time scales. The chaoticity is encoded in the time between oscillations of the norm, which can be chosen in any prescribed way. This phenomenon, that we name as effective chaos (it occurs over a long, but finite, time scale), is consequence of the existence of symbolic dynamics for an effective system. Since the first order resonant dynamics has been proved to be essentially stable, we need to perform a second order analysis to find an effective model displaying chaotic dynamics. More precisely, after some reductions, this model behaves as two weakly coupled pendulums.
SCRIMP: Scalable Communication for Reinforcement- and Imitation-Learning-Based Multi-Agent Pathfinding

Trading off performance guarantees in favor of scalability, the Multi-Agent Path Finding (MAPF) community has recently started to embrace Multi-Agent Reinforcement Learning (MARL), where agents learn to collaboratively generate individual, collision-free (but often suboptimal) paths. Scalability is usually achieved by assuming a local field of view (FOV) around the agents, helping scale to arbitrary world sizes. However, this assumption significantly limits the amount of information available to the agents, making it difficult for them to enact the type of joint maneuvers needed in denser MAPF tasks. In this paper, we propose SCRIMP, where agents learn individual policies from even very small (down to $3\mathrm{x}3$) FOVs, by relying on a highly-scalable global communication mechanism based on a modified transformer. We further equip agents with a state-value-based tie-breaking strategy to further improve performance in symmetric situations, and introduce intrinsic rewards to encourage exploration while mitigating the long-term credit assignment problem. Empirical evaluations on a set of experiments indicate that SCRIMP can achieve higher performance with improved scalability compared to other state-of-the-art learning-based MAPF planners with larger FOVs, and even yields similar performance as a classical centralized planner in many cases. Ablation studies further validate the effectiveness of our proposed techniques. Finally, we show that our trained model can be directly implemented on real robots for online MAPF through high-fidelity simulations in gazebo.
New Chondritic Bodies Identified in Eight Oxygen-bearing White Dwarfs

We present observations and analyses of eight white dwarf stars (WDs) that have accreted rocky material from their surrounding planetary systems. The spectra of these helium-atmosphere WDs contain detectable optical lines of all four major rock-forming elements (O, Mg, Si, and Fe). This work increases the sample of oxygen-bearing WDs with parent body composition analyses by roughly 33%. To first order, the parent bodies that have been accreted by the eight WDs are similar to those of chondritic meteorites in relative elemental abundances and oxidation states. Seventy-five percent of the WDs in this study have observed oxygen excesses implying volatiles in the parent bodies with abundances similar to those of chondritic meteorites. Three WDs have oxidation states that imply more reduced material than found in CI chondrites, indicating the possible detection of Mercury-like parent bodies, but are less constrained. These results contribute to the recurring conclusion that extrasolar rocky bodies closely resemble those in our solar system, and do not, as a whole, yield unusual or unique compositions.
Creation of Single Vacancies in hBN with Electron Irradiation.

Understanding electron irradiation effects is vital not only for reliable transmission electron microscopy characterization, but increasingly also for the controlled manipulation of 2D materials. The displacement cross sections of monolayer hexagonal boron nitride (hBN) are measured using aberration-corrected scanning transmission electron microscopy in near ultra-high vacuum at primary beam energies between 50 and 90 keV. Damage rates below 80 keV are up to three orders of magnitude lower than previously measured at edges under poorer residual vacuum conditions, where chemical etching appears to dominate. Notably, it is possible to create single vacancies in hBN using electron irradiation, with boron almost twice as likely as nitrogen to be ejected below 80 keV. Moreover, any damage at such low energies cannot be explained by elastic knock-on, even when accounting for the vibrations of the atoms. A theoretical description is developed to account for the lowering of the displacement threshold due to valence ionization resulting from inelastic scattering of probe electrons, modeled using charge-constrained density functional theory molecular dynamics. Although significant reductions are found depending on the constrained charge, quantitative predictions for realistic ionization states are currently not possible. Nonetheless, there is potential for defect-engineering of hBN at the level of single vacancies using electron irradiation.
D2Q-DETR: Decoupling and Dynamic Queries for Oriented Object Detection with Transformers

Despite the promising results, existing oriented object detection methods usually involve heuristically designed rules, e.g., RRoI generation, rotated NMS. In this paper, we propose an end-to-end framework for oriented object detection, which simplifies the model pipeline and obtains superior performance. Our framework is based on DETR, with the box regression head replaced with a points prediction head. The learning of points is more flexible, and the distribution of points can reflect the angle and size of the target rotated box. We further propose to decouple the query features into classification and regression features, which significantly improves the model precision. Aerial images usually contain thousands of instances. To better balance model precision and efficiency, we propose a novel dynamic query design, which reduces the number of object queries in stacked decoder layers without sacrificing model performance. Finally, we rethink the label assignment strategy of existing DETR-like detectors and propose an effective label re-assignment strategy for improved performance. We name our method D2Q-DETR. Experiments on the largest and challenging DOTA-v1.0 and DOTA-v1.5 datasets show that D2Q-DETR outperforms existing NMS-based and NMS-free oriented object detection methods and achieves the new state-of-the-art.
Improved Analysis of the Breakup Corrections to the High Energy $^3$He Beam Polarization measurements with HJET

The requirements for hadron polarimetry at the future Electron Ion Collider (EIC) include measurements of the absolute helion ($^3$He, $h$) beam polarization with systematic uncertainties better than $\sigma^\text{syst}_P/P\le1\%$. Recently, it was suggested to utilize the Polarized Atomic Hydrogen Gas Jet Target (HJET) for precision measurement of the polarization of the $\sim$100 GeV/n helion beam. At the Relativistic Heavy Ion Collider, HJET serves to determine absolute proton beam polarization with low systematic uncertainties of about $\delta^\text{syst}P/P\lesssim0.5\%$. To adapt the HJET method for the EIC helion beam, the experimentally determined ratio of the beam and target (jet) spin correlated asymmetries should be adjusted by the ratio of $p^\uparrow{h}$ and $h^\uparrow{p}$ analyzing powers $A_\text{N}^{ph}(t)/A_\text{N}^{hp}(t)$ which, in the leading order approximation, is predefined by magnetic moments of the proton and helion, $(\mu_p-1)/(\mu_h/2-1/3)$. However, to achieve the required accuracy in the measured polarization, the corrections due to hadronic spin-flip amplitudes and due to possible beam $^3$He breakup should be considered. Here a more accurate analysis of the possible breakup corrections to the measured $^3$He beam polarization is provided. The results confirm that the breakup corrections are negligible for the EIC helion beam absolute polarization measurement by HJET.
Strain‐energy effects on dynamic fragmentation

Grady’s model of the dynamic fragmentation process, in which the average fragment size is determined by balancing the local kinetic energy and the surface energy, is modified to include the stored elastic (strain) energy. The revised model predicts that the strain energy should dominate for brittle materials, with low fracture toughness and high fracture‐initiation stress. This conclusion is not borne out, however, by limited experimental data on brittle steels, even when the kinetic‐energy density is small compared with the strain‐energy density.
How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks

The GPT-3.5 models have demonstrated impressive performance in various Natural Language Processing (NLP) tasks, showcasing their strong understanding and reasoning capabilities. However, their robustness and abilities to handle various complexities of the open world have yet to be explored, which is especially crucial in assessing the stability of models and is a key aspect of trustworthy AI. In this study, we perform a comprehensive experimental analysis of GPT-3.5, exploring its robustness using 21 datasets (about 116K test samples) with 66 text transformations from TextFlint that cover 9 popular Natural Language Understanding (NLU) tasks. Our findings indicate that while GPT-3.5 outperforms existing fine-tuned models on some tasks, it still encounters significant robustness degradation, such as its average performance dropping by up to 35.74\% and 43.59\% in natural language inference and sentiment analysis tasks, respectively. We also show that GPT-3.5 faces some specific robustness challenges, including robustness instability, prompt sensitivity, and number sensitivity. These insights are valuable for understanding its limitations and guiding future research in addressing these challenges to enhance GPT-3.5's overall performance and generalization abilities.
TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders

Enhancing the expressive capacity of deep learning-based time series models with self-supervised pre-training has become ever-increasingly prevalent in time series classification. Even though numerous efforts have been devoted to developing self-supervised models for time series data, we argue that the current methods are not sufficient to learn optimal time series representations due to solely unidirectional encoding over sparse point-wise input units. In this work, we propose TimeMAE, a novel self-supervised paradigm for learning transferrable time series representations based on transformer networks. The distinct characteristics of the TimeMAE lie in processing each time series into a sequence of non-overlapping sub-series via window-slicing partitioning, followed by random masking strategies over the semantic units of localized sub-series. Such a simple yet effective setting can help us achieve the goal of killing three birds with one stone, i.e., (1) learning enriched contextual representations of time series with a bidirectional encoding scheme; (2) increasing the information density of basic semantic units; (3) efficiently encoding representations of time series using transformer networks. Nevertheless, it is a non-trivial to perform reconstructing task over such a novel formulated modeling paradigm. To solve the discrepancy issue incurred by newly injected masked embeddings, we design a decoupled autoencoder architecture, which learns the representations of visible (unmasked) positions and masked ones with two different encoder modules, respectively. Furthermore, we construct two types of informative targets to accomplish the corresponding pretext tasks. One is to create a tokenizer module that assigns a codeword to each masked region, allowing the masked codeword classification (MCC) task to be completed effectively.
Solar cell with an intermediate band of finite width

This article considers idealized solar cells whose absorbers are intermediate band (IB) media with finite bandwidths that permit both interband and intraband photoinduced electronic transitions at states within the IB. To comprehend the effect of the IB width, three classes of IB absorbers are constructed where each class is distinguished from the others by its spectral selectivity. It is shown that (i) the maximum-power efficiency tends gradually toward zero with increasing bandwidth when photoinduced interband transitions and intraband transitions are equally likely; (ii) with respect to the former, a relative efficiency enhancement may occur when photoinduced intraband transitions dominate interband transitions; and (iii) although thermodynamically consistent, efficiencies may be physically inconsistent without including photoinduced intraband transitions. Resulting from the solar surface temperature of 6000 K, the authors conclude that the largest efficiencies result when the IB width is roughly equal to or less than 800 meV.
Information Plane Analysis for Dropout Neural Networks

The information-theoretic framework promises to explain the predictive power of neural networks. In particular, the information plane analysis, which measures mutual information (MI) between input and representation as well as representation and output, should give rich insights into the training process. This approach, however, was shown to strongly depend on the choice of estimator of the MI. The problem is amplified for deterministic networks if the MI between input and representation is infinite. Thus, the estimated values are defined by the different approaches for estimation, but do not adequately represent the training process from an information-theoretic perspective. In this work, we show that dropout with continuously distributed noise ensures that MI is finite. We demonstrate in a range of experiments that this enables a meaningful information plane analysis for a class of dropout neural networks that is widely used in practice.
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning

Privacy and security concerns in real-world applications have led to the development of adversarially robust federated models. However, the straightforward combination between adversarial training and federated learning in one framework can lead to the undesired robustness deterioration. We discover that the attribution behind this phenomenon is that the generated adversarial data could exacerbate the data heterogeneity among local clients, making the wrapped federated learning perform poorly. To deal with this problem, we propose a novel framework called Slack Federated Adversarial Training (SFAT), assigning the client-wise slack during aggregation to combat the intensified heterogeneity. Theoretically, we analyze the convergence of the proposed method to properly relax the objective when combining federated learning and adversarial training. Experimentally, we verify the rationality and effectiveness of SFAT on various benchmarked and real-world datasets with different adversarial training and federated optimization methods. The code is publicly available at https://github.com/ZFancy/SFAT.
Mapping state transition susceptibility in quantum annealing

Quantum annealing is a novel type of analog computation that aims to use quantum mechanical fluctuations to search for optimal solutions of Ising problems. Quantum annealing in the transverse field Ising model, implemented on D-Wave devices, works by applying a time dependent transverse field, which puts all qubits into a uniform state of superposition, and then applying a Hamiltonian over time which describes a user programmed Ising problem. We present a method which utilizes two control features of D-Wave quantum annealers, reverse annealing and an h-gain schedule, to quantify the susceptibility, or the distance, between two classical states of an Ising problem. The starting state is encoded using reverse annealing, and the second state is encoded on the linear terms of problem Hamiltonian. An h-gain schedule is specified which incrementally increases the strength of the linear terms, thus allowing a quantification of the h-gain strength required to transition the anneal into a specific state at the final measurement. By the nature of quantum annealing, the state tends towards global minima and therefore we restrict the second classical state to a minimum solution of the given Ising problem. This susceptibility mapping, when enumerated across all initial states, shows in detail the behavior of the quantum annealer during reverse annealing. The procedure is experimentally demonstrated on three small test Ising's which were embedded in parallel on the D-Wave Advantage_system4. 1. Analysis of the state transition mapping shows detailed characteristics of the reverse annealing process including intermediate state transition paths, which are visually represented as state transition networks.
General Eigenstate Thermalization via Free Cumulants in Quantum Lattice Systems

The Eigenstate-Thermalization-Hypothesis (ETH) has been established as the general framework to understand quantum statistical mechanics. Only recently has the attention been paid to so-called general ETH, which accounts for higher-order correlations among matrix elements, and that can be rationalized theoretically using the language of Free Probability. In this work, we perform the first numerical investigation of the general ETH in physical many-body systems with local interactions by testing the decomposition of higher-order correlators into free cumulants. We perform exact diagonalization on two classes of local non-integrable (chaotic) quantum many-body systems: spin chain Hamiltonians and Floquet brickwork unitary circuits. We show that the dynamics of four-time correlation functions are encoded in fourth-order free cumulants, as predicted by ETH. Their non-trivial frequency dependence encodes the physical properties of local many-body systems and distinguishes them from structureless, rotationally invariant ensembles of random matrices.
Measured and predicted oblique waves in a wave basin

Measurements of the wave field corresponding to regular, oblique waves produced by a segmented wave generator have been made. Contours of wave height and wave phase have been obtained for several wave periods and directions, both with and without the presence of a large cylinder in the wave basin. These are compared with the predictions of a computer model based on linear diffraction theory. The comparison is generally quite favourable, with the principal features of the wave height and phase plots being reproduced by the computer model. In contrast to the snake principle, the numerical method appears able to account adequately for the effects of wave diffraction and reflection.
Robust wall modes and their interplay with bulk turbulence in confined rotating Rayleigh-Bénard convection

In confined rotating convection, a strong zonal flow can develop close to the side wall with a modal structure that precesses anti-cyclonically (counter to the applied rotation) along the side wall. It is surmised that this is a robust non-linear evolution of the wall modes observed before the onset of bulk convection. Here, we perform direct numerical simulations of cylindrically confined rotating convection at high rotation rates and strong turbulent forcing. Through comparison with earlier work, we find a fit-parameter-free relation that links the angular drift frequency of the robust wall mode observed far into the turbulent regime with the critical wall mode frequency at onset, firmly substantiating the connection between the observed boundary zonal flow and the wall modes. Deviations from this relation at stronger turbulent forcing suggest early signs of the bulk turbulence starting to hamper the development of the wall mode. Furthermore, by studying the interactive flow between the robust wall mode and the bulk turbulence, we identify radial jets penetrating from the wall mode into the bulk. These jets induce a large scale multipolar vortex structure in the bulk turbulence, dependent on the wavenumber of the wall mode. In a narrow cylinder the entire bulk flow is dominated by a quadrupolar vortex driven by the radial jets, while in a wider cylinder the jets are found to have a finite penetration length and the vortices do not cover the entire bulk. We also identify the role of Reynolds stresses in the generation of zonal flows in the region near the sidewall.
Shocks and Photoionization of the Inner 650 au Jet of the Interacting Binary Star R Aquarii from Multiwavelength Hubble Space Telescope Observations

Astrophysical jets are present in a range of environments, including young stellar objects, X-ray binaries, and active galactic nuclei, but their formation is still not fully understood. As one of the nearest symbiotic binary stars, R Aquarii (D ∼ 220 pc) offers a unique opportunity to study the inner region within ∼600 au of the jet source, which is particularly crucial to our understanding of nonrelativistic jet formation and origin. We present high-angular resolution UV and optical imaging from the Hubble Space Telescope in six emission-line regions of the inner jet. Using these observations to obtain a range of representative line ratios for our system and kinematic data derived from a comparison with previous studies, we model the shocked gas in order to determine the relative roles of shock heating and photoionization in the R Aquarii system. We find that our shock models suggest that a nonzero magnetic field is needed to describe the measured line ratios. We also find that the Mg ii λλ2795,2802 intensities are overpredicted by our models for most of the jet regions, perhaps because of depletion onto grains or to opacity in these resonance lines.
SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks

Prompt tuning is a technology that tunes a small set of parameters to steer a pre-trained language model (LM) to directly generate the output for downstream tasks. Recently, prompt tuning has demonstrated its storage and computation efficiency in both natural language processing (NLP) and speech processing fields. These advantages have also revealed prompt tuning as a candidate approach to serving pre-trained LM for multiple tasks in a unified manner. For speech processing, SpeechPrompt shows its high parameter efficiency and competitive performance on a few speech classification tasks. However, whether SpeechPrompt is capable of serving a large number of tasks is unanswered. In this work, we propose SpeechPrompt v2, a prompt tuning framework capable of performing a wide variety of speech classification tasks, covering multiple languages and prosody-related tasks. The experiment result shows that SpeechPrompt v2 achieves performance on par with prior works with less than 0.15M trainable parameters in a unified framework.