Miracle-dz/UniFAR-FTUs huggingface dataset:
InstructVideo: Instructing Video Diffusion Models with Human Feedback

Diffusion models have emerged as the de facto paradigm for video generation. However, their reliance on web-scale data of varied quality often yields results that are visually unappealing and misaligned with the textual prompts. To tackle this problem, we propose InstructVideo to instruct text-to-video diffusion models with human feedback by reward fine-tuning. InstructVideo has two key ingredients: 1) To ameliorate the cost of reward fine-tuning induced by generating through the full DDIM sampling chain, we recast reward fine-tuning as editing. By leveraging the diffusion process to corrupt a sampled video, InstructVideo requires only partial inference of the DDIM sampling chain, reducing fine-tuning cost while improving fine-tuning efficiency. 2) To mitigate the absence of a dedicated video reward model for human preferences, we repurpose established image reward models, e.g., HPSv2. To this end, we propose Segmental Video Reward, a mechanism to provide reward signals based on segmental sparse sampling, and Temporally Attenuated Reward, a method that mitigates temporal modeling degradation during fine-tuning. Extensive experiments, both qualitative and quantitative, validate the practicality and efficacy of using image reward models in InstructVideo, significantly enhancing the visual quality of generated videos without compromising generalization capabilities. Code and models will be made publicly available.
Cross-Modal Reasoning with Event Correlation for Video Question Answering

Video Question Answering (VideoQA) is a very attractive and challenging research direction aiming to understand complex semantics of heterogeneous data from two domains, i.e., the spatio-temporal video content and the word sequence in question. Although various attention mechanisms have been utilized to manage contextualized representations by modeling intra- and inter-modal relationships of the two modalities, one limitation of the predominant VideoQA methods is the lack of reasoning with event correlation, that is, sensing and analyzing relationships among abundant and informative events contained in the video. In this paper, we introduce the dense caption modality as a new auxiliary and distill event-correlated information from it to infer the correct answer. To this end, we propose a novel end-to-end trainable model, Event-Correlated Graph Neural Networks (EC-GNNs), to perform cross-modal reasoning over information from the three modalities (i.e., caption, video, and question). Besides the exploitation of a brand new modality, we employ cross-modal reasoning modules for explicitly modeling inter-modal relationships and aggregating relevant information across different modalities, and we propose a question-guided self-adaptive multi-modal fusion module to collect the question-oriented and event-correlated evidence through multi-step reasoning. We evaluate our model on two widely-used benchmark datasets and conduct an ablation study to justify the effectiveness of each proposed component.
Quantifying Bias in Text-to-Image Generative Models

Bias in text-to-image (T2I) models can propagate unfair social representations and may be used to aggressively market ideas or push controversial agendas. Existing T2I model bias evaluation methods only focus on social biases. We look beyond that and instead propose an evaluation methodology to quantify general biases in T2I generative models, without any preconceived notions. We assess four state-of-the-art T2I models and compare their baseline bias characteristics to their respective variants (two for each), where certain biases have been intentionally induced. We propose three evaluation metrics to assess model biases including: (i) Distribution bias, (ii) Jaccard hallucination and (iii) Generative miss-rate. We conduct two evaluation studies, modelling biases under general, and task-oriented conditions, using a marketing scenario as the domain for the latter. We also quantify social biases to compare our findings to related works. Finally, our methodology is transferred to evaluate captioned-image datasets and measure their bias. Our approach is objective, domain-agnostic and consistently measures different forms of T2I model biases. We have developed a web application and practical implementation of what has been proposed in this work, which is at https://huggingface.co/spaces/JVice/try-before-you-bias. A video series with demonstrations is available at https://www.youtube. com/channel/UCk-0xyUyT0MSd_hkp4jQt1Q
TagCLIP: A Local-to-Global Framework to Enhance Open-Vocabulary Multi-Label Classification of CLIP Without Training

Contrastive Language-Image Pre-training (CLIP) has demonstrated impressive capabilities in open-vocabulary classification. The class token in the image encoder is trained to capture the global features to distinguish different text descriptions supervised by contrastive loss, making it highly effective for single-label classification. However, it shows poor performance on multi-label datasets because the global feature tends to be dominated by the most prominent class and the contrastive nature of softmax operation aggravates it. In this study, we observe that the multi-label classification results heavily rely on discriminative local features but are overlooked by CLIP. As a result, we dissect the preservation of patch-wise spatial information in CLIP and proposed a local-to-global framework to obtain image tags. It comprises three steps: (1) patch-level classification to obtain coarse scores; (2) dual-masking attention refinement (DMAR) module to refine the coarse scores; (3) class-wise reidentification (CWR) module to remedy predictions from a global perspective. This framework is solely based on frozen CLIP and significantly enhances its multi-label classification performance on various benchmarks without dataset-specific training. Besides, to comprehensively assess the quality and practicality of generated tags, we extend their application to the downstream task, i.e., weakly supervised semantic segmentation (WSSS) with generated tags as image-level pseudo labels. Experiments demonstrate that this classify-then-segment paradigm dramatically outperforms other annotation-free segmentation methods and validates the effectiveness of generated tags. Our code is available at https://github.com/linyq2117/TagCLIP.
On the sparsity of integer matrices with a given discriminant

We consider the set $\mathcal M_n(\mathbb Z; H)$ of $n\times n$-matrices with integer elements of size at most $H$ and obtain upper bounds on the number of matrices from $\mathcal M_n(\mathbb Z; H)$, for which the characteristic polynomial has a fixed discriminant $d$. When $d=0$, this corresponds to counting matrices with a repeated eigenvalue, and thus is related to counting non-diagonalisable matrices. For $d\ne 0$, this problem seems not to have been studied previously, while for $d=0$, both our approach and the final result improve on those of A. J. Hetzel, J. S. Liew and K. Morrison (2007).
Structure of the 
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 resonance

We examine the internal structure of the $\Lambda(1670)$ through an analysis of lattice QCD simulations and experimental data within Hamiltonian effective field theory. Two scenarios are presented. The first describes the $\Lambda(1670)$ as a bare three-quark basis state, which mixes with the $\pi\Sigma$, $\bar{K}N$, $\eta\Lambda$ and $K\Xi$ meson-baryon channels. In the second scenario, the $\Lambda(1670)$ is dynamically generated from these isospin-0 coupled channels. The $K^-p$ scattering data and the pole structures of the $\Lambda(1405)$ and the $\Lambda(1670)$ can be simultaneously described well in both scenarios. However, a comparison of the finite-volume spectra to lattice QCD calculations reveals significant differences between these scenarios, with a clear preference for the first case. Thus the lattice QCD results play a crucial role in allowing us to distinguish between these two scenarios for the internal structure of the $\Lambda(1670)$.
An integrated EOS, pore-crush, strength and damage model framework for near-field ground-shock

An integrated Equation of State (EOS) and strength/pore-crush/damage model framework is provided for modeling near to source (near-field) ground-shock response, where large deformations and pressures necessitate coupling EOS with pressure-dependent plastic yield and damage. Nonlinear pressure-dependence of strength up to high-pressures is combined with a Modified Cam-Clay-like cap-plasticity model in a way to allow degradation of strength from pore-crush damage, what we call the"Yp-Cap"model. Nonlinear hardening under compaction allows modeling the crush-out of pores in combination with a fully saturated EOS, i.e., for modeling partially saturated ground-shock response, where air-filled voids crush. Attention is given to algorithmic clarity and efficiency of the provided model, and the model is employed in example numerical simulations, including finite element simulations of underground explosions to exemplify its robustness and utility.
On the galactic origin of ultra high energy cosmic rays

It is shown that the acceleration of particles by a powerful relativistic jet associated with the activity of a supermassive black hole in the Galactic center several million years ago may explain the observed cosmic ray spectrum at energies higher than $10^{15}$ eV. The accelerated particles are efficiently confined in the extended magnetized gas halo created by the supernova and central black hole activity just after the Galaxy formation. We found that both the heavy and light chemical composition of ultra-high energy cosmic rays can be consistent with observations.
A novel boundary-integral algorithm for nonlinear unsteady surface and interfacial waves

We devise a new time-stepping algorithm for two-dimensional nonlinear unsteady surface and interfacial waves. The algorithm uses Cauchy's integral formula, which only requires information on the interface, to solve Laplace equation by using iterative techniques. We derive Eulerian and mixed Eulerian-Lagrangian descriptions by using arclength to parameterize the interface which is updated through its inclination angle and velocity potential at each time step. The algorithm shows broad applicability and excellent numerical accuracy in various numerical simulations, including wave breaking, collisions of solitary waves, vortex roll-up, etc. We especially focus on the stability of symmetric interfacial gravity-capillary solitary waves in deep water. Linear stability analysis is performed using a new formulation which possesses excellent numerical efficiency and robustness. It is shown that the depression/elevation solitary waves are linearly stable/unstable except the portion where the monotonicity of energy curve changes firstly. These results are supported by our fully nonlinear simulations, especially the head-on collisions of solitary waves.
All but One: Surgical Concept Erasing with Model Preservation in Text-to-Image Diffusion Models

Text-to-Image models such as Stable Diffusion have shown impressive image generation synthesis, thanks to the utilization of large-scale datasets. However, these datasets may contain sexually explicit, copyrighted, or undesirable content, which allows the model to directly generate them. Given that retraining these large models on individual concept deletion requests is infeasible, fine-tuning algorithms have been developed to tackle concept erasing in diffusion models. While these algorithms yield good concept erasure, they all present one of the following issues: 1) the corrupted feature space yields synthesis of disintegrated objects, 2) the initially synthesized content undergoes a divergence in both spatial structure and semantics in the generated images, and 3) sub-optimal training updates heighten the model's susceptibility to utility harm. These issues severely degrade the original utility of generative models. In this work, we present a new approach that solves all of these challenges. We take inspiration from the concept of classifier guidance and propose a surgical update on the classifier guidance term while constraining the drift of the unconditional score term. Furthermore, our algorithm empowers the user to select an alternative to the erasing concept, allowing for more controllability. Our experimental results show that our algorithm not only erases the target concept effectively but also preserves the model’s generation capability.
Quantum cohomology of the Hilbert scheme of points on an elliptic surface

We determine the quantum multiplication with divisor classes on the Hilbert scheme of points on an elliptic surface $S \to \Sigma$ for all curve classes which are contracted by the induced fibration $S^{[n]} \to \Sigma^{[n]}$. The formula is expressed in terms of explicit operators on Fock space. The structure constants are meromorphic quasi-Jacobi forms of index $0$. Combining with work of Hu-Li-Qin, this determines the quantum multiplication with divisors on the Hilbert scheme of elliptic surfaces with $p_g(S)>0$. We also determine the equivariant quantum multiplication with divisor classes for the Hilbert scheme of points on the product $E \times \mathbb{C}$. The proof of our formula is based on Nesterov's Hilb/PT wall-crossing, a newly established GW/PT correspondence for the product of an elliptic surface times a curve, and new computations in the Gromov-Witten theory of an elliptic curve.
Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation

Continual Test-Time Adaptation (CTTA) is proposed to migrate a source pre-trained model to continually changing target distributions, addressing real-world dynamism. Existing CTTA methods mainly rely on entropy minimization or teacher-student pseudo-labeling schemes for knowledge extraction in unlabeled target domains. However, dynamic data distributions cause miscalibrated predictions and noisy pseudo-labels in existing self-supervised learning methods, hindering the effective mitigation of error accumulation and catastrophic forgetting problems during the continual adaptation process. To tackle these issues, we propose a continual self-supervised method, Adaptive Distribution Masked Autoencoders (ADMA), which enhances the extraction of target domain knowledge while mitigating the accumulation of distribution shifts. Specifically, we propose a Distribution-aware Masking (DaM) mechanism to adaptively sample masked positions, followed by establishing consistency constraints between the masked target samples and the original target samples. Additionally, for masked tokens, we utilize an efficient decoder to reconstruct a hand-crafted feature descriptor (e.g., Histograms of Oriented Gradients), leveraging its invariant properties to boost task-relevant representations. Through conducting extensive experiments on four widely recognized benchmarks, our proposed method attains state-of-the-art performance in both classification and segmentation CTTA tasks. Our project page: https://sites.google. com/view/continual-mae/home.
Emergence of Fairness Behavior Driven by Reputation-Based Voluntary Participation in Evolutionary Dictator Games

Recently, reputation-based indirect reciprocity has been widely applied to the study on fairness behavior. Previous works mainly investigate indirect reciprocity by considering compulsory participation. While in reality, individuals may choose voluntary participation according to the opponent's reputation. It is still unclear how such reputation-based voluntary participation influences the evolution of fairness. To address this question, we introduce indirect reciprocity with voluntary participation into the dictator game (DG). We respectively consider good dictators or recipients can voluntarily participate in games when the opponents are assessed as bad. We theoretically calculate the fairness level under all social norms of third-order information. Our findings reveal that several social norms induce the high fairness level in both scenarios. However, more social norms lead to a high fairness level for voluntary participation of recipients, compared with the one of good dictators. The results also hold when the probability of voluntary participation is not low. Our results demonstrate that recipients' voluntary participation is more effective in promoting the emergence of fairness behavior.
Modélisation numérique du comportement d’un sol gonflant chargé soumis à des variations hydriques

Les phenomenes de retrait-gonflement des sols argileux se manifestent par des desordres affectant principalement des maisons individuelles souvent peu rigides et fondees superficiellement. En effet, l’alternance des periodes de secheresse et de precipitation entraine des modifications de l’etat hydrique du sol se traduisant par des deplacements des fondations superficielles. Cette alternance de retrait et de gonflement peut provoquer des degâts dans les bâtis sous forme de fissures, voire entrainer la rupture partielle ou totale de l’ouvrage, lorsqu’ils ne sont pas pris en compte dans les projets. Dans cet article, le modele elastoplastique BExM (Barcelona Expansive Model), pour les sols gonflants non satures implante dans le code de calcul aux elements finis Code_Bright, est utilise pour l’etude du comportement hydromecanique d’un sol gonflant charge par une semelle filante. L’objectif de cette etude est d’analyser l’influence des sollicitations hydriques dues a la variation des conditions climatiques (precipitation et evaporation) sur les deplacements du massif de sol argileux, et d’etudier l’influence d’une geomembrane posee dans le sol afin de retarder les transferts hydriques et les phenomenes de retrait-gonflement. Les resultats obtenus montrent que le modele est capable de predire qualitativement les deplacements du massif de sol pendant les differentes sollicitations hydriques.
Flux Quantization on Phase Space

While it has become widely appreciated that (higher) gauge theories need, besides their variational phase space data, to be equipped with"flux quantization laws"in generalized differential cohomology, there used to be no general prescription for how to define and construct the resulting flux-quantized phase space stacks. In this short note we observe that all higher Maxwell-type equations have solution spaces given by flux densities on a Cauchy surface subject to a higher Gauss law and no further constraint: The metric duality-constraint is all absorbed into the evolution equation away from the Cauchy surface. Moreover, we observe that the higher Gauss law characterizes the Cauchy data as flat differential forms valued in a characteristic L-infinity-algebra. Using the recent construction of the non-abelian Chern-Dold character map, this implies that compatible flux quantization laws on phase space have classifying spaces whose rational Whitehead L-infinity algebra is this characteristic one. The flux-quantized higher phase space stack of the theory is then simply the corresponding (generally non-abelian) differential cohomology moduli stack on the Cauchy surface. We show how this systematic prescription subsumes existing proposals for flux-quantized phase spaces of vacuum Maxwell theory and of the chiral boson. Moreover, for the case of NS/RR-fields in type II supergravity, the traditional"Hypothesis K"of flux quantization in topological K-theory is naturally implied, without the need, on phase space, for the notorious further duality constraint. Finally, as a genuinely non-abelian example, we consider flux-quantization of the C-field in 11d supergravity/M-theory given by unstable differential 4-Cohomotopy ("Hypothesis H") and emphasize again that, implemented on Cauchy data, this qualifies as the full phase space without the need for a further duality constraint.
Principal Component Copulas for Capital Modelling

We introduce a class of copulas that we call Principal Component Copulas. This class intends to combine the strong points of copula-based techniques with principal component-based models, which results in flexibility when modelling tail dependence along the most important directions in multivariate data. The proposed techniques have conceptual similarities and technical differences with the increasingly popular class of factor copulas. Such copulas can generate complex dependence structures and also perform well in high dimensions. We show that Principal Component Copulas give rise to practical and technical advantages compared to other techniques. We perform a simulation study and apply the copula to multivariate return data. The copula class offers the possibility to avoid the curse of dimensionality when estimating very large copula models and it performs particularly well on aggregate measures of tail risk, which is of importance for capital modeling.
ClassLIE: Structure- and Illumination-Adaptive Classification for Low-Light Image Enhancement

Low-light images often suffer from limited visibility and multiple types of degradation, rendering low-light image enhancement (LIE) a non-trivial task. Some endeavors have been recently made to enhance low-light images using convolutional neural networks (CNNs). However, they have low efficiency in learning the structural information and diverse illumination levels at the local regions of an image. Consequently, the enhanced results are affected by unexpected artifacts, such as unbalanced exposure, blur, and color bias. To this end, this paper proposes a novel framework, called ClassLIE, that combines the potential of CNNs and transformers. It classifies and adaptively learns the structural and illumination information from the low-light images in a holistic and regional manner, thus showing better enhancement performance. Our framework first employs a structure and illumination classification (SIC) module to learn the degradation information adaptively. In SIC, we decompose an input image into an illumination map and a reflectance map. A class prediction block is then designed to classify the degradation information by calculating the structure similarity scores on the reflectance map and mean square error on the illumination map. As such, each input image can be divided into patches with three enhancement difficulty levels. Then, a feature learning and fusion (FLF) module is proposed to adaptively learn the feature information with CNNs for different enhancement difficulty levels while learning the long-range dependencies for the patches in a holistic manner. Experiments on five benchmark datasets consistently show our ClassLIE achieves new state-of-the-art performance, with 25.74 PSNR and 0.92 SSIM on the LOL dataset.