Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing orbits of stellar mass objects deep in galactic nuclei with quasi-periodic eruptions

Quasi-periodic eruptions (QPEs) are intense repeating soft X-ray bursts with recurrence times about a few to ten hours from nearby galactic nuclei. The origin of QPEs is still unclear. In this work, we investigated the extreme mass ratio inspiral (EMRI) + accretion disk model, where the disk is formed from a previous tidal disruption event (TDE). In this EMRI+TDE disk model, the QPEs are the result of collisions between a TDE disk and a stellar mass object (a stellar mass black hole or a main sequence star) orbiting around a supermassive black hole (SMBH) in galactic nuclei. This model is flexible and comprehensive in recovering different aspects of QPE observations. If this interpretation is correct, QPEs will be invaluable in probing the orbits of stellar mass objects in the vicinity of SMBHs, and further inferring the formation of EMRIs which is one the primary targets of spaceborne gravitational wave missions. Taking GSN 069 as an example, we find the EMRI wherein is of low eccentricity ($e<0.1$ at 3-$\sigma$ confidence level) and semi-major axis about $O(10^2)$ gravitational radii of the central SMBH, which is consistent with the prediction of the wet EMRI formation channel, while incompatible with alternatives.
Improving convergence of Generalised Rosenbluth sampling for branched polymer models by uniform sampling

Sampling with the Generalised Atmospheric Rosenbluth Method (GARM) is a technique for estimating the distributions of lattice polymer models that has had some success in the study of linear polymers and lattice polygons. In this paper we will explain how and why such sampling appears not to be effective for many models of branched polymers. Analysing the algorithm on a simple binary tree, we argue that the fundamental issue is an inherent bias towards extreme configurations that is costly to correct with reweighting techniques. We provide a solution to this by applying uniform sampling methods to the atmospheres that are central to GARM. We caution that the ensuing computational complexity often outweighs the improvements gained.
Using spatial extreme-value theory with machine learning to model and understand spatially compounding extremes

When extreme weather events affect large areas, their regional to sub-continental spatial scale is important for their impacts. We propose a novel methodology that combines spatial extreme-value theory with a machine learning (ML) algorithm to model weather extremes and quantify probabilities associated with the occurrence, intensity and spatial extent of these events. The model is here applied to Western European summertime heat extremes. Using new loss functions adapted to extreme values, we fit a theoretically-motivated spatial model to extreme positive temperature anomaly fields from 1959-2022, using the daily 500-hpa geopotential height fields across the Euro-Atlantic region and the local soil moisture as predictors. Our generative model reveals the importance of individual circulation features in determining different facets of heat extremes, thereby enriching our process understanding of them from a data-driven perspective. The occurrence, intensity, and spatial extent of heat extremes are sensitive to the relative position of individual ridges and troughs that are part of a large-scale wave pattern. Heat extremes in Europe are thus the result of a complex interplay between local and remote physical processes. Our approach is able to extrapolate beyond the range of the data to make risk-related probabilistic statements, and applies more generally to other weather extremes. It also offers an attractive alternative to physical model-based techniques, or to ML approaches that optimise scores focusing on predicting well the bulk instead of the tail of the data distribution.
A study of experimental sensitivities to nucleon parton distributions with xFitter

In collider physics, parton distribution functions (PDFs) play a crucial role in computing theoretical cross sections for scattering reactions. This study explores how different experimental data sets influence extracted PDFs in CTEQ-TEA and MSHT NNLO PDF analyses. To gauge the impact of experimental data, including the HERA and ZEUS combined charm and beauty production, LHCb 7 TeV charm and beauty production, and CMS 2.76 TeV W+c production, I utilize the $L_2$ sensitivity in the Hessian framework as a visual representation of their respective impacts. This sensitivity quantifies the statistical pulls on individual data sets against the best-fit PDFs, facilitating the identification of tensions among competing data sets. Using the QCD fitting framework xFitter, I extract the necessary values for plotting $L_2$ sensitivities for eight distinct data sets implemented in the program, employing recent PDF sets from the CTEQ-TEA and MSHT groups. The computed $L_2$ sensitivities estimate the potential impact of the examined data sets.
Knowledge Distillation on Spatial-Temporal Graph Convolutional Network for Traffic Prediction

Efficient real-time traffic prediction is crucial for reducing transportation time. To predict traffic conditions, we employ a spatio-temporal graph neural network (ST-GNN) to model our real-time traffic data as temporal graphs. Despite its capabilities, it often encounters challenges in delivering efficient real-time predictions for real-world traffic data. Recognizing the significance of timely prediction due to the dynamic nature of real-time data, we employ knowledge distillation (KD) as a solution to enhance the execution time of ST-GNNs for traffic prediction. In this paper, We introduce a cost function designed to train a network with fewer parameters (the student) using distilled data from a complex network (the teacher) while maintaining its accuracy close to that of the teacher. We use knowledge distillation, incorporating spatial-temporal correlations from the teacher network to enable the student to learn the complex patterns perceived by the teacher. However, a challenge arises in determining the student network architecture rather than considering it inadvertently. To address this challenge, we propose an algorithm that utilizes the cost function to calculate pruning scores, addressing small network architecture search issues, and jointly fine-tunes the network resulting from each pruning stage using KD. Ultimately, we evaluate our proposed ideas on two real-world datasets, PeMSD7 and PeMSD8. The results indicate that our method can maintain the student's accuracy close to that of the teacher, even with the retention of only $3\%$ of network parameters.
Estrutura da vegetação de lagoa associada à vereda no Cerrado brasileiro

This is the first report on aquatic macrophytes in ponds of veredas (palm swamps) in the Cerrado (Brazilian savanna). We studied a pond in a vereda in Terenos, Mato Grosso do Sul state, central-western Brazil, during drought and flood periods to assess plant community structure and the relationship between species distribution and water depth. Sampling was carried out in five permanent transects, along which we placed sampling units of 1 m2 quadrats every 5 m. We estimated visually the relative cover of each species, bare ground, dead matter, and exposed water. We also measured water depth. We recorded 77 species of 49 genera and 31 families. The number of species varied between seasons, and in the rainy season the amphibious life form was predominant. The richest families were Poaceae (15 species), Cyperaceae (11), Asteraceae (4) and Lentibulariaceae (4). The richest genera were Rhynchospora (4), Utricularia (4), Eleocharis (4), Aeschynomene (3), Bacopa (3), Ludwigia (3) and Panicum (3). We found six new occurrences for the cerrado. Eleocharis acutangula had the highest relative cover (rainy and dry seasons), followed by Bacopa reflexa (rainy season), Paspalum lenticulare (rainy and dry seasons), Leersia hexandra (rainy and dry seasons), and Sagittaria rhombifolia (rainy season). Some species were distributed mainly at shallower depths, others occurred in deeper water, whereas some species varied greatly in terms of depth. Heliotropium filiforme and Ludwigia nervosa were found only in the dry season. In the rainy season, as expected, the exclusive species were mostly true aquatics.
Homogenization of high-contrast dielectric elastomer composites

This paper focuses on the homogenization of high-contrast dielectric elastomer composites, materials that deform in response to electrical stimulation. The considered heterogeneous material consisting of an ambient material with inserted particles is described by a weakly coupled system of an electrostatic equation with an elastic equation enriched with electrostriction. It is assumed that particles gradually become rigid as the fine-scale parameter approaches zero. This study demonstrates that the effective response of this system entails a homogeneous dielectric elastomer, described by a weakly coupled system of PDEs. The coefficients of the homogenized equations are dependent on various factors, including the composite's geometry, the original microstructure's periodicity, and the coefficients characterizing the initial heterogeneous material. Particularly, these coefficients are significantly influenced by the high-contrast nature of the fine-scale problem's coefficients. Consequently, as anticipated, the high-contrast coefficients of the original yield non-local effects in the homogenized response.
Keep Decoding Parallel with Effective Knowledge Distillation from Language Models to End-to-end Speech Recognisers

This study presents a novel approach for knowledge distillation (KD) from a BERT teacher model to an automatic speech recognition (ASR) model using intermediate layers. To distil the teacher's knowledge, we use an attention decoder that learns from BERT's token probabilities. Our method shows that language model (LM) information can be more effectively distilled into an ASR model using both the intermediate layers and the final layer. By using the intermediate layers as distillation target, we can more effectively distil LM knowledge into the lower network layers. Using our method, we achieve better recognition accuracy than with shallow fusion of an external LM, allowing us to maintain fast parallel decoding. Experiments on the LibriSpeech dataset demonstrate the effectiveness of our approach in enhancing greedy decoding with connectionist temporal classification (CTC).
Out-of-Distribution Detection & Applications With Ablated Learned Temperature Energy

As deep neural networks become adopted in high-stakes domains, it is crucial to be able to identify when inference inputs are Out-of-Distribution (OOD) so that users can be alerted of likely drops in performance and calibration despite high confidence. Among many others, existing methods use the following two scores to do so without training on any apriori OOD examples: a learned temperature and an energy score. In this paper we introduce Ablated Learned Temperature Energy (or"AbeT"for short), a method which combines these prior methods in novel ways with effective modifications. Due to these contributions, AbeT lowers the False Positive Rate at $95\%$ True Positive Rate (FPR@95) by $35.39\%$ in classification (averaged across all ID and OOD datasets measured) compared to state of the art without training networks in multiple stages or requiring hyperparameters or test-time backward passes. We additionally provide empirical insights as to how our model learns to distinguish between In-Distribution (ID) and OOD samples while only being explicitly trained on ID samples via exposure to misclassified ID examples at training time. Lastly, we show the efficacy of our method in identifying predicted bounding boxes and pixels corresponding to OOD objects in object detection and semantic segmentation, respectively - with an AUROC increase of $5.15\%$ in object detection and both a decrease in FPR@95 of $41.48\%$ and an increase in AUPRC of $34.20\%$ on average in semantic segmentation compared to previous state of the art.
Lattice Boltzmann simulation of non-equilibrium flows using spectral multiple-relaxation-time collision model

Prediction of non-equilibrium flows is critical to space flight. In the present work we demonstrate that the recently developed spectral multiple-relaxation-time (SMRT) lattice Boltzmann (LB) model is theoretically equivalent to Grad's eigen-system [Grad, H. , In Thermodynamics of Gases (1958)] where the eigen-functions obtained by tensor decomposition of the Hermite polynomials are also those of the linearized Boltzmann equation. Numerical results of shock structure simulation using Maxwell molecular model agree very well with those of a high-resolution fast spectral method code up to Mach 7 provided that the relaxation times of the irreducible tensor components match their theoretical values. If a reduced set of relaxation times are used such as in the Shakhov model and lumped-sum relaxation of Hermite modes, non-negligible discrepancies starts to occur as Mach number is raised, indicating the necessity of the fine-grained relaxation model. Together with the proven advantages of LB, the LB-SMRT scheme offers a competitive alternative for non-equilibrium flow simulation.
Feature Denoising Diffusion Model for Blind Image Quality Assessment

Blind Image Quality Assessment (BIQA) aims to evaluate image quality in line with human perception, without reference benchmarks. Currently, deep learning BIQA methods typically depend on using features from high-level tasks for transfer learning. However, the inherent differences between BIQA and these high-level tasks inevitably introduce noise into the quality-aware features. In this paper, we take an initial step towards exploring the diffusion model for feature denoising in BIQA, namely Perceptual Feature Diffusion for IQA (PFD-IQA), which aims to remove noise from quality-aware features. Specifically, (i) We propose a {Perceptual Prior Discovery and Aggregation module to establish two auxiliary tasks to discover potential low-level features in images that are used to aggregate perceptual text conditions for the diffusion model. (ii) We propose a Perceptual Prior-based Feature Refinement strategy, which matches noisy features to predefined denoising trajectories and then performs exact feature denoising based on text conditions. Extensive experiments on eight standard BIQA datasets demonstrate the superior performance to the state-of-the-art BIQA methods, i.e., achieving the PLCC values of 0.935 ( vs. 0.905 in KADID) and 0.922 ( vs. 0.894 in LIVEC).
How Robust Are Energy-Based Models Trained With Equilibrium Propagation?

Deep neural networks (DNNs) are easily fooled by adversarial perturbations that are imperceptible to humans. Adversarial training, a process where adversarial examples are added to the training set, is the current state-of-the-art defense against adversarial attacks, but it lowers the model's accuracy on clean inputs, is computationally expensive, and offers less robustness to natural noise. In contrast, energy-based models (EBMs), which were designed for efficient implementation in neuromorphic hardware and physical systems, incorporate feedback connections from each layer to the previous layer, yielding a recurrent, deep-attractor architecture which we hypothesize should make them naturally robust. Our work is the first to explore the robustness of EBMs to both natural corruptions and adversarial attacks, which we do using the CIFAR-10 and CIFAR-100 datasets. We demonstrate that EBMs are more robust than transformers and display comparable robustness to adversarially-trained DNNs on gradient-based (white-box) attacks, query-based (black-box) attacks, and natural perturbations without sacrificing clean accuracy, and without the need for adversarial training or additional training techniques.
Generalizing Speaker Verification for Spoof Awareness in the Embedding Space

It is now well-known that automatic speaker verification (ASV) systems can be spoofed using various types of adversaries. The usual approach to counteract ASV systems against such attacks is to develop a separate spoofing countermeasure (CM) module to classify speech input either as a bonafide, or a spoofed utterance. Nevertheless, such a design requires additional computation and utilization efforts at the authentication stage. An alternative strategy involves a single monolithic ASV system designed to handle both zero-effort imposter (non-targets) and spoofing attacks. Such spoof-aware ASV systems have the potential to provide stronger protections and more economic computations. To this end, we propose to generalize the standalone ASV (G-SASV) against spoofing attacks, where we leverage limited training data from CM to enhance a simple backend in the embedding space, without the involvement of a separate CM module during the test (authentication) phase. We propose a novel yet simple backend classifier based on deep neural networks and conduct the study via domain adaptation and multi-task integration of spoof embeddings at the training stage. Experiments are conducted on the ASVspoof 2019 logical access dataset, where we improve the performance of statistical ASV backends on the joint (bonafide and spoofed) and spoofed conditions by a maximum of 36.2% and 49.8% in terms of equal error rates, respectively.
CodeTailor: Personalized Parsons Puzzles are Preferred Over AI-Generated Solutions to Support Learning

Programming can be challenging for novices, and it is difficult to provide high-quality, comprehensive, and timely support at scale. Generative AI and its products, like ChatGPT, can create a solution for most introductory programming problems. However, students may become overly reliant on these tools for quick code generation and homework completion, which might cause reduced engagement and limited learning. In this work, we present CodeTailor, a system that leverages large language models (LLMs) while still encouraging students' cognitive engagement. CodeTailor provides a personalized Parsons puzzle to support struggling students. In a Parsons puzzle, students place mixed-up code blocks in the correct order to solve it. A technical evaluation with 800 incorrect student code demonstrated that CodeTailor can efficiently create high-quality (correct, personalized, and concise) Parsons puzzles for students. In a within-subjects experiment with 18 novice programmers, students rated using CodeTailor as more engaging, and they recalled more newly acquired elements from the supported practice in the posttest after using CodeTailor, compared to when they simply received an AI-generated solution. In addition, most students preferred to use CodeTailor over receiving an AI-generated solution to support learning. Qualitative observations and interviews also provided evidence for the benefits of CodeTailor, including emphasizing thinking about solution construction, fostering continuity in learning, promoting reflection, and boosting student confidence. We conclude by suggesting future design ideas for applying generative AI to facilitate active learning opportunities and minimize over-reliance.
Spatial Structure Constraints for Weakly Supervised Semantic Segmentation

The image-level label has prevailed in weakly supervised semantic segmentation tasks due to its easy availability. Since image-level labels can only indicate the existence or absence of specific categories of objects, visualization-based techniques have been widely adopted to provide object location clues. Considering class activation maps (CAMs) can only locate the most discriminative part of objects, recent approaches usually adopt an expansion strategy to enlarge the activation area for more integral object localization. However, without proper constraints, the expanded activation will easily intrude into the background region. In this paper, we propose spatial structure constraints (SSC) for weakly supervised semantic segmentation to alleviate the unwanted object over-activation of attention expansion. Specifically, we propose a CAM-driven reconstruction module to directly reconstruct the input image from deep CAM features, which constrains the diffusion of last-layer object attention by preserving the coarse spatial structure of the image content. Moreover, we propose an activation self-modulation module to refine CAMs with finer spatial structure details by enhancing regional consistency. Without external saliency models to provide background clues, our approach achieves 72.7% and 47.0% mIoU on the PASCAL VOC 2012 and COCO datasets, respectively, demonstrating the superiority of our proposed approach. The source codes and models have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/SSC.
Compton scattering study of strong orbital delocalization in a 
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 cathode

Cobalt is used in Li-ion batteries, but it is expensive and could be replaced by nickel to deliver better performance at a lower cost. With this motivation, we discuss how the character of redox orbitals of LiNiO$_2$ can be ascertained through x-ray Compton scattering measurements combined with parallel first-principles simulations. Our analysis reveals the nature of hole states in Li-doped NiO resulting from the hybridization of O 2$p$ and Ni 3$d$ orbitals. Our study also gives insight into the ferromagnetic ground state and provides a pathway toward the rational design of next-generation battery materials.
Reentrant quantum anomalous Hall effect in molecular beam epitaxy-grown MnBi2Te4 thin films

In this study, we investigate intrinsic magnetic topological insulator MnBi2Te4 thin films grown by molecular beam epitaxy. We observe a reentrant quantum anomalous Hall effect when the Fermi energy enters the valance band and magnetic field equals zero, indicating the emergence of the Chern Anderson insulator state. The discovery opens a new avenue for realizing the QAH effect and underscores the fundamental role of both Berry curvature and Anderson localization.
Mastering Text-to-Image Diffusion: Recaptioning, Planning, and Generating with Multimodal LLMs

Diffusion models have exhibit exceptional performance in text-to-image generation and editing. However, existing methods often face challenges when handling complex text prompts that involve multiple objects with multiple attributes and relationships. In this paper, we propose a brand new training-free text-to-image generation/editing framework, namely Recaption, Plan and Generate (RPG), harnessing the powerful chain-of-thought reasoning ability of multimodal LLMs to enhance the compositionality of text-to-image diffusion models. Our approach employs the MLLM as a global planner to decompose the process of generating complex images into multiple simpler generation tasks within subregions. We propose complementary regional diffusion to enable region-wise compositional generation. Furthermore, we integrate text-guided image generation and editing within the proposed RPG in a closed-loop fashion, thereby enhancing generalization ability. Extensive experiments demonstrate our RPG outperforms state-of-the-art text-to-image diffusion models, including DALL-E 3 and SDXL, particularly in multi-category object composition and text-image semantic alignment. Notably, our RPG framework exhibits wide compatibility with various MLLM architectures (e.g., MiniGPT-4) and diffusion backbones (e.g., ControlNet). Our code is available at: https://github.com/YangLing0818/RPG-DiffusionMaster