Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple change point detection in tensors

This paper proposes a criterion for detecting change structures in tensor data. To accommodate tensor structure with structural mode that is not suitable to be equally treated and summarized in a distance to measure the difference between any two adjacent tensors, we define a mode-based signal-screening Frobenius distance for the moving sums of slices of tensor data to handle both dense and sparse model structures of the tensors. As a general distance, it can also deal with the case without structural mode. Based on the distance, we then construct signal statistics using the ratios with adaptive-to-change ridge functions. The number of changes and their locations can then be consistently estimated in certain senses, and the confidence intervals of the locations of change points are constructed. The results hold when the size of the tensor and the number of change points diverge at certain rates, respectively. Numerical studies are conducted to examine the finite sample performances of the proposed method. We also analyze two real data examples for illustration.
The Possibility of Modeling the Very High Energy Afterglow of GRB 221009A in a Wind Environment

In this paper, we model the dynamics and radiation physics of the rarity event GRB 221009A afterglow in detail. By introducing a top-hat jet that propagates in an environment dominated by stellar winds, we explain the publicly available observations of afterglow associated with GRB 221009A over the first week. It is predicted that GRB 221009A emits a luminous very high energy afterglow based on the synchrotron self-Compton (SSC) process in our model. We show the broadband spectral energy distribution (SED) analysis results of GRB 221009A and find that the SSC radiation component of GRB 221009A is very bright in the 0.1–10 TeV band. The integrated SED shows that the SSC emission in the TeV band has detection sensitivity significantly higher than that of LHASSO, MAGIC, and CTA. However, since the release of further observations, deviations from the standard wind environment model have gradually shown up in data. For example, the late-time multiband afterglow cannot be consistently explained under the standard wind environment scenario. It may be necessary to consider modeling with a structured jet with complex geometry or a partial revision of the standard model. Furthermore, we find that the inclusion of GeV observations could break the degeneracy between model parameters, highlighting the significance of high-energy observations in determining accurate parameters for GRB afterglows.
The Hα Broadband Photometric Reverberation Mapping of Four Seyfert 1 Galaxies

Broadband photometric reverberation mapping (PRM) has been investigated for active galactic nuclei (AGNs) in recent years, but mostly on accretion disk continuum RM. Due to the small fraction of broad emission lines in the broad band, PRM for emission lines is very challenging. Here, we present an ICCF-Cut method for broadband PRM to obtain the Hα broad-line lag and apply it to four Seyfert 1 galaxies: MCG+08-11-011, NGC 2617, 3C 120, and NGC 5548. All of them have high-quality broadband lightcurves with daily/subdaily cadences, which enable us to extract Hα lightcurves from the line band by subtracting the contributions from the continuum and host galaxy. Their extracted Hα lightcurves are compared with the lagged continuum-band lightcurves, as well as the lagged Hβ lightcurves obtained by spectroscopic RM (SRM) at the same epochs. The consistency of these lightcurves and the comparison with the SRM Hβ lags provide support for the Hα lags of these AGNs, in a range from 9 to 19 days, obtained by the ICCF-Cut, JAVELIN, and χ 2 methods. The simulations for evaluating the reliability of the Hα lags and the comparisons between the SRM Hβ and PRM Hα lags indicate that the consistency of the ICCF-Cut, JAVELIN, and χ 2 results can ensure the reliability of the derived Hα lags. These methods may be used to estimate the broad-line region sizes and black hole masses of a large sample of AGNs in large multi-epoch, high-cadence photometric surveys such as LSST in the future.
The method of densities for non-isotropic Boolean models

This thesis deals with the Boolean model, a basic model of stochastic geometry for the description of porous structures like the pore space in sand stone. The main result is a formula which gives in two and three dimensions a series representation of the most important model parameter, the intensity, using densities of so-called harmonic intrinsic volumes, which are new observable geometric quantities.
An Empirical Evaluation of Federated Contextual Bandit Algorithms

As the adoption of federated learning increases for learning from sensitive data local to user devices, it is natural to ask if the learning can be done using implicit signals generated as users interact with the applications of interest, rather than requiring access to explicit labels which can be difficult to acquire in many tasks. We approach such problems with the framework of federated contextual bandits, and develop variants of prominent contextual bandit algorithms from the centralized seting for the federated setting. We carefully evaluate these algorithms in a range of scenarios simulated using publicly available datasets. Our simulations model typical setups encountered in the real-world, such as various misalignments between an initial pre-trained model and the subsequent user interactions due to non-stationarity in the data and/or heterogeneity across clients. Our experiments reveal the surprising effectiveness of the simple and commonly used softmax heuristic in balancing the well-know exploration-exploitation tradeoff across the breadth of our settings.
MXM-CLR: A Unified Framework for Contrastive Learning of Multifold Cross-Modal Representations

Multifold observations are common for different data modalities, e.g., a 3D shape can be represented by multi-view images and an image can be described with different captions. Existing cross-modal contrastive representation learning (XM-CLR) methods such as CLIP are not fully suitable for multifold data as they only consider one positive pair and treat other pairs as negative when computing the contrastive loss. In this paper, we propose MXM-CLR, a unified framework for contrastive learning of multifold cross-modal representations. MXM-CLR explicitly models and learns the relationships between multifold observations of instances from different modalities for more comprehensive representation learning. The key of MXM-CLR is a novel multifold-aware hybrid loss which considers multiple positive observations when computing the hard and soft relationships for the cross-modal data pairs. We conduct quantitative and qualitative comparisons with SOTA baselines for cross-modal retrieval tasks on the Text2Shape and Flickr30K datasets. We also perform extensive evaluations on the adaptability and generalizability of MXM-CLR, as well as ablation studies on the loss design and effects of batch sizes. The results show the superiority of MXM-CLR in learning better representations for the multifold data. The code is available at https://github.com/JLU-ICL/MXM-CLR.
Neutron diffraction study of the chiral magnet MnGe

Uncollinear magnets yield the possibility of couplingelectric and magnetic degrees of freedom, a key ingredientforbuildingnovelelectronicdevices.Thisprocessisatplayintheso-calledtype-IImultiferroicmaterialswherethecouplingbetween electric and magnetic order parameters, arising frominverse Dzyaloshinskii-Moriya
Dynamics of kink clusters for scalar fields in dimension 1+1

We consider a real scalar field equation in dimension 1+1 with an even positive self-interaction potential having two non-degenerate zeros (vacua) 1 and -1. It is known that such a model admits non-trivial static solutions called kinks and antikinks. A kink cluster is a solution approaching, for large positive times, a superposition of alternating kinks and antikinks whose velocities converge to 0. They can be equivalently characterised as the solutions of minimal possible energy containing a given number of transitions between the vacua, or as the solutions whose kinetic energy decays to 0 for large time. Our main result is a determination of the main-order asymptotic behaviour of any kink cluster. Moreover, we construct a kink cluster for any prescribed initial positions of the kinks and antikinks, provided that their mutual distances are sufficiently large. Finally, we show that kink clusters are universal profiles for the formation/collapse of multi-kink configurations. The proofs rely on a reduction, using appropriately chosen modulation parameters, to an n-body problem with attractive exponential interactions.
Facial Affective Analysis based on MAE and Multi-modal Information for 5th ABAW Competition

Human affective behavior analysis focuses on analyzing human expressions or other behaviors, which helps improve the understanding of human psychology. The CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW) is dedicated to providing high-quality and large-scale Aff-wild2 and Hume-Reaction datasets for the recognition of commonly used emotion representations, such as Action Units (AU), basic expression categories, Valence-Arousal (VA), and Emotional Reaction Intensity (ERI). The competition strives to make signiﬁcant efforts toward improving the accuracy and applicability of affective analysis research in real-world scenarios. In this paper, we introduce our submission to the CVPR 2023: ABAW5. Our approach involves several key components. First, we utilize the visual information from an MAE model that has been pre-trained on a large-scale face image dataset in a self-supervised manner. Next, we ﬁne-tune the MAE encoder on the ABAW challenges using single frames from the Aff-wild2 dataset. Additionally, we leverage the multi-modal and temporal information from the videos and implement a transformer-based framework to fuse the multi-modal features. To further enhance model generalization, we introduce a novel two-branch collaboration training strategy that randomly interpolates the logits space. Our approach is supported by extensive quantitative experiments and ablation studies conducted on the Aff-Wild2 dataset and Hume-Reaction dataset, which demonstrate the effectiveness of our proposed method.
Ratio-limit boundaries for random walks on relatively hyperbolic groups

We study boundaries arising from limits of ratios of transition probabilities for random walks on relatively hyperbolic groups. We extend, as well as determine significant limitations of, a strategy employed by Woess for computing ratio-limit boundaries for the class of hyperbolic groups. On the one hand we employ results of the second and third authors to adapt this strategy to spectrally non-degenerate random walks, and show that the closure of minimal points in $R$-Martin boundary is the unique smallest invariant subspace in ratio-limit boundary. On the other hand we show that the general strategy can fail when the random walk is spectrally degenerate and adapted on a free product. Using our results, we are able to extend a theorem of the first author beyond the hyperbolic case and establish the existence of a co-universal quotient for Toeplitz C*-algebras arising from random walks which are spectrally non-degenerate on relatively hyperbolic groups. Finally, we exhibit an example of a relatively hyperbolic group carrying two random walks such that the ratio limit boundaries are not equivariantly homeomorphic and no two equivariant quotients of their respective Toeplitz C*-algebras are equivariantly $*$-isomorphic.
Stellar-mass black holes in the Hyades star cluster?

Astrophysical models of binary-black hole mergers in the Universe require a significant fraction of stellar-mass black holes (BHs) to receive negligible natal kicks to explain the gravitational wave detections. This implies that BHs should be retained even in open clusters with low escape velocities (≲ 1 km s−1). We search for signatures of the presence of BHs in the nearest open cluster to the Sun – the Hyades – by comparing density profiles of direct N-body models to data from Gaia. The observations are best reproduced by models with 2–3 BHs at present. Models that never possessed BHs have an half-mass radius $\sim 30~{{\ \rm per\ cent}}$ smaller than the observed value, while those where the last BHs were ejected recently (≲ 150 Myr ago) can still reproduce the density profile. In 50% of the models hosting BHs, we find BHs with stellar companion(s). Their period distribution peaks at ∼103 yr, making them unlikely to be found through velocity variations. We look for potential BH companions through large Gaia astrometric and spectroscopic errors, identifying 56 binary candidates - none of which consistent with a massive compact companion. Models with 2–3 BHs have an elevated central velocity dispersion, but observations can not yet discriminate. We conclude that the present-day structure of the Hyades requires a significant fraction of BHs to receive natal kicks smaller than the escape velocity of ∼3 km s−1 at the time of BH formation and that the nearest BHs to the Sun are in, or near, Hyades.
Temperature-dependent Eu spin reorientations in the tetragonal A-type antiferromagnet EuGa$_4$ induced by small ab-plane magnetic fields

The body-centered-tetragonal antiferromagnet EuGa$_4$ exhibits A-type antiferromagnetic order below its N\'eel temperature $T_{\rm N} = 16.4$ K in magnetic field $H = 0$ where the moments are ferromagnetically aligned in the $ab$-plane with the Eu moments in adjacent Eu planes aligned antiferromagnetically. Previous magnetization versus field $M_{ab}(H)$ measurements revealed that the moments exhibit a spin-reorientation transition at a critical field $H_{c1}$ where the Eu moments become perpendicular to an in-plane magnetic field while still remaining in the $ab$ plane. A theory for $T=0$ K was previously presented that successfully explained the observed low-field moment-reorientation behavior at $T = 2$ K. Here we present a theory explaining the observed $T$ dependence of $M_{ab}(H,T<T_{\rm N})$ in the [1,0,0] direction for $H\leq H_{\rm c1}(T)$ from 2 to 14 K arising from a $T$-dependent anisotropy energy.
Unit Scaling: Out-of-the-Box Low-Precision Training

We present unit scaling, a paradigm for designing deep learning models that simplifies the use of low-precision number formats. Training in FP16 or the recently proposed FP8 formats offers substantial efficiency gains, but can lack sufficient range for out-of-the-box training. Unit scaling addresses this by introducing a principled approach to model numerics: seeking unit variance of all weights, activations and gradients at initialisation. Unlike alternative methods, this approach neither requires multiple training runs to find a suitable scale nor has significant computational overhead. We demonstrate the efficacy of unit scaling across a range of models and optimisers. We further show that existing models can be adapted to be unit-scaled, training BERT-Large in FP16 and then FP8 with no degradation in accuracy.
Evaluating Language Models for Knowledge Base Completion

Structured knowledge bases (KBs) are a foundation of many intelligent applications, yet are notoriously incomplete. Language models (LMs) have recently been proposed for unsupervised knowledge base completion (KBC), yet, despite encouraging initial results, questions regarding their suitability remain open. Existing evaluations often fall short because they only evaluate on popular subjects, or sample already existing facts from KBs. In this work, we introduce a novel, more challenging benchmark dataset, and a methodology tailored for a realistic assessment of the KBC potential of LMs. For automated assessment, we curate a dataset called WD-KNOWN, which provides an unbiased random sample of Wikidata, containing over 3.9 million facts. In a second step, we perform a human evaluation on predictions that are not yet in the KB, as only this provides real insights into the added value over existing KBs. Our key finding is that biases in dataset conception of previous benchmarks lead to a systematic overestimate of LM performance for KBC. However, our results also reveal strong areas of LMs. We could, for example, perform a significant completion of Wikidata on the relations nativeLanguage, by a factor of ~21 (from 260k to 5.8M) at 82% precision, usedLanguage, by a factor of ~2.1 (from 2.1M to 6.6M) at 82% precision, and citizenOf by a factor of ~0.3 (from 4.2M to 5.3M) at 90% precision. Moreover, we find that LMs possess surprisingly strong generalization capabilities: even on relations where most facts were not directly observed in LM training, prediction quality can be high.
Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural Networks

Deep neural networks (DNNs) are often trained on the premise that the complete training data set is provided ahead of time. However, in real-world scenarios, data often arrive in chunks over time. This leads to important considerations about the optimal strategy for training DNNs, such as whether to fine-tune them with each chunk of incoming data (warm-start) or to retrain them from scratch with the entire corpus of data whenever a new chunk is available. While employing the latter for training can be resource-intensive, recent work has pointed out the lack of generalization in warm-start models. Therefore, to strike a balance between efficiency and generalization, we introduce Learn, Unlearn, and Relearn (LURE) an online learning paradigm for DNNs. LURE interchanges between the unlearning phase, which selectively forgets the undesirable information in the model through weight reinitialization in a data-dependent manner, and the relearning phase, which emphasizes learning on generalizable features. We show that our training paradigm provides consistent performance gains across datasets in both classification and few-shot settings. We further show that it leads to more robust and well-calibrated models.
Nearly geodesic immersions and domains of discontinuity

We study nearly geodesic immersions in higher rank symmetric spaces of non-compact type, which we define as immersions that satisfy a bound on their fundamental form, generalizing the notion of immersions in hyperbolic space with principal curvature in $(-1,1)$. This notion depends on the choice of a flag manifold embedded in the visual boundary, and immersions satisfying this bound admit a natural domain in this flag manifold that comes with a fibration. As an application we give an explicit fibration of some domains of discontinuity for some Anosov representations. Our method can be applied in particular to some $\Theta$-positive representations for each notion of $\Theta$-positivity.
SKED: Sketch-guided Text-based 3D Editing

Text-to-image diffusion models are gradually introduced into computer graphics, recently enabling the development of Text-to-3D pipelines in an open domain. However, for interactive editing purposes, local manipulations of content through a simplistic textual interface can be arduous. Incorporating user guided sketches with Text-to-image pipelines offers users more intuitive control. Still, as state-of-the-art Text-to-3D pipelines rely on optimizing Neural Radiance Fields (NeRF) through gradients from arbitrary rendering views, conditioning on sketches is not straightforward. In this paper, we present SKED, a technique for editing 3D shapes represented by NeRFs. Our technique utilizes as few as two guiding sketches from different views to alter an existing neural field. The edited region respects the prompt semantics through a pre-trained diffusion model. To ensure the generated output adheres to the provided sketches, we propose novel loss functions to generate the desired edits while preserving the density and radiance of the base instance. We demonstrate the effectiveness of our proposed method through several qualitative and quantitative experiments. https://sked-paper.github. io/
Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation

Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semi-supervised semantic segmentation methods with application domains such as autonomous driving. Existing work very often employs relatively large segmentation backbone networks to improve segmentation accuracy, at the expense of computational costs. In addition, many use uniform sampling to reduce ground truth data requirements for learning needed, often resulting in sub-optimal performance. To address these issues, we propose a new pipeline that employs a smaller architecture, requiring fewer ground-truth annotations to achieve superior segmentation accuracy compared to contemporary approaches. This is facilitated via a novel Sparse Depthwise Separable Convolution module that significantly reduces the network parameter count while retaining overall task performance. To effectively sub-sample our training data, we propose a new Spatio-Temporal Redundant Frame Downsampling (ST-RFD) method that leverages knowledge of sensor motion within the environment to extract a more diverse subset of training data frame samples. To leverage the use of limited annotated data samples, we further propose a soft pseudo-label method informed by LiDAR reflectivity. Our method outperforms contemporary semi-supervised work in terms of mIoU, using less labeled data, on the SemanticKITTI (59.5@5%) and ScribbleKITTI (58.1@5%) benchmark datasets, based on a 2.3x reduction in model parameters and 641x fewer multiply-add operations whilst also demonstrating significant performance improvement on limited training data (i.e., Less is More).
Fully Self-Supervised Depth Estimation from Defocus Clue

Depth-from-defocus (DFD), modeling the relationship between depth and defocus pattern in images, has demonstrated promising performance in depth estimation. Recently, several self-supervised works try to overcome the difficulties in acquiring accurate depth ground-truth. However, they depend on the all-in-focus (AIF) images, which cannot be captured in real-world scenarios. Such limitation discourages the applications of DFD methods. To tackle this issue, we propose a completely self-supervised framework that estimates depth purely from a sparse focal stack. We show that our framework circumvents the needs for the depth and AIF image ground-truth, and receives superior predictions, thus closing the gap between the theoretical success of DFD works and their applications in the real world. In particular, we propose (i) a more realistic setting for DFD tasks, where no depth or AIF image ground-truth is available; (ii) a novel self- supervision framework that provides reliable predictions of depth and AIF image under the challenging setting. The proposed framework uses a neural model to predict the depth and AIF image, and utilizes an optical model to validate and refine the prediction. We verify our framework on three benchmark datasets with rendered focal stacks and real focal stacks. Qualitative and quantitative evaluations show that our method provides a strong baseline for self- supervised DFD tasks. The source code is publicly avail- able at https://github.com/Ehzoahis/DEReD.