Miracle-dz/UniFAR-FTUs huggingface dataset:
SinFusion: Training Diffusion Models on a Single Image or Video

Diffusion models exhibited tremendous progress in image and video generation, exceeding GANs in quality and diversity. However, they are usually trained on very large datasets and are not naturally adapted to manipulate a given input image or video. In this paper we show how this can be resolved by training a diffusion model on a single input image or video. Our image/video-specific diffusion model (SinFusion) learns the appearance and dynamics of the single image or video, while utilizing the conditioning capabilities of diffusion models. It can solve a wide array of image/video-specific manipulation tasks. In particular, our model can learn from few frames the motion and dynamics of a single input video. It can then generate diverse new video samples of the same dynamic scene, extrapolate short videos into long ones (both forward and backward in time) and perform video upsampling. Most of these tasks are not realizable by current video-specific generation methods.
PartAL: Efficient Partial Active Learning in Multi-Task Visual Settings

Multi-task learning is central to many real-world applications. Unfortunately, obtaining labelled data for all tasks is time-consuming, challenging, and expensive. Active Learning (AL) can be used to reduce this burden. Existing techniques typically involve picking images to be annotated and providing annotations for all tasks. In this paper, we show that it is more effective to select not only the images to be annotated but also a subset of tasks for which to provide annotations at each AL iteration. Furthermore, the annotations that are provided can be used to guess pseudo-labels for the tasks that remain unannotated. We demonstrate the effectiveness of our approach on several popular multi-task datasets.
Limit distribution theory for $f$-Divergences

$f$-divergences, which quantify discrepancy between probability distributions, are ubiquitous in information theory, machine learning, and statistics. While there are numerous methods for estimating $f$-divergences from data, a limit distribution theory, which quantifies fluctuations of the estimation error, is largely obscure. As limit theorems are pivotal for valid statistical inference, to close this gap, we develop a general methodology for deriving distributional limits for $f$-divergences based on the functional delta method and Hadamard directional differentiability. Focusing on four prominent $f$-divergences -- Kullback-Leibler divergence, $\chi^2$ divergence, squared Hellinger distance, and total variation distance -- we identify sufficient conditions on the population distributions for the existence of distributional limits and characterize the limiting variables. These results are used to derive one- and two-sample limit theorems for Gaussian-smoothed $f$-divergences, both under the null and the alternative. Finally, an application of the limit distribution theory to auditing differential privacy is proposed and analyzed for significance level and power against local alternatives.
CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation

Practical dialog systems need to deal with various knowledge sources, noisy user expressions, and the shortage of annotated data. To better solve the above problems, we propose CGoDial, a new challenging and comprehensive Chinese benchmark for multi-domain Goal-oriented Dialog evaluation. It contains 96,763 dialog sessions, and 574,949 dialog turns totally, covering three datasets with different knowledge sources: 1) a slot-based dialog (SBD) dataset with table-formed knowledge, 2) a flow-based dialog (FBD) dataset with tree-formed knowledge, and a retrieval-based dialog (RBD) dataset with candidate-formed knowledge. To bridge the gap between academic benchmarks and spoken dialog scenarios, we either collect data from real conversations or add spoken features to existing datasets via crowd-sourcing. The proposed experimental settings include the combinations of training with either the entire training set or a few-shot training set, and testing with either the standard test set or a hard test subset, which can assess model capabilities in terms of general prediction, fast adaptability and reliable robustness.
ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference

Whereas the ability of deep networks to produce useful predictions has been amply demonstrated, estimating the reliability of these predictions remains challenging. Sampling approaches such as MC-Dropout and Deep Ensembles have emerged as the most popular ones for this purpose. Unfortunately, they require many forward passes at inference time, which slows them down. Sampling-free approaches can be faster but suffer from other drawbacks, such as lower reliability of uncertainty estimates, difficulty of use, and limited applicability to different types of tasks and data. In this work, we introduce a sampling-free approach that is generic and easy to deploy, while producing reliable uncertainty estimates on par with state-of-the-art methods at a significantly lower computational cost. It is predicated on training the network to produce the same output with and without additional information about it. At inference time, when no prior information is given, we use the network's own prediction as the additional information. We then take the distance between the predictions with and without prior information as our uncertainty measure. We demonstrate our approach on several classification and regression tasks. We show that it delivers results on par with those of Ensembles but at a much lower computational cost.
Data-based determination of the isospin-limit light-quark-connected contribution to the anomalous magnetic moment of the muon

We describe how recent determinations of exclusive-mode contributions to $a_\mu^{\rm LO,HVP}$, the leading-order hadronic vacuum polarization contribution to the anomalous magnetic moment of the muon, can be used to provide, up to small electromagnetic (EM) corrections accessible from the lattice, a data-based dispersive determination of $a_\mu^{\rm lqc;\, IL}$, the isospin-limit, light-quark-connected contribution to $a_\mu^{\rm LO,HVP}$. Such a determination is of interest in view of the existence of a number of lattice results for this quantity, emerging evidence for a tension between lattice and dispersive determinations of $a_\mu^{\rm LO,HVP}$, and the desire to clarify the source of this tension. Taking as input for the small EM correction that must be applied to the purely data-driven dispersive determination the result $-0.93(58)\times 10^{-10}$ obtained in a recent BMW lattice study, we find $a_\mu^{\rm lqc;\, IL}$ to be $635.0(2.7)\times 10^{-10}$ if the results of Keshavarzi, Nomura and Teubner are used for the exclusive-mode contributions and $638.4(4.1)\times 10^{-10}$ if instead those of Davier, H\"ocker, Malaescu and Zhang are used.
Fluctuation-response relation of time-symmetric quantities around general nonequilibrium stationary state

A connection between the response and fluctuation in general nonequilibrium stationary states is investigated. We focus on time-symmetric quantities and find that the fluctuation of a kind of empirical measure can be expressed with the response of the empirical measure, current, and the time-symmetric current. This relation is proven by using the fictitious stalling decomposition: we decompose a single observed transition (edge in the state space) between two microscopic states into two transitions such that one of the transitions stalls in this stationary state. Through this trick, relations for stalling stationary states apply to general nonequilibrium stationary states, which leads to the desired relation.
Deep inelastic scattering from unpolarised targets

In this paper the experimental status of unpolarised structure functions is reviewed. In particular the latest results from the NMC, E665, CCFR, and HERA experiments are discussed. Emphasis is put on the fixed-target experiments, which cover with high precision the x region relevant for the present polarised DIS experiments.
ElegantSeg: End-to-End Holistic Learning for Extra-Large Image Semantic Segmentation

This paper presents a new paradigm for E xtra-l arg e ima g e sem ant ic Seg mentation, called ElegantSeg , that ca-pably processes holistic extra-large image semantic segmentation (ELISS). The extremely large sizes of extra-large images (ELIs) tend to cause GPU memory exhaustion. To tackle this issue, prevailing works either follow the global-local fusion pipeline or conduct the multi-stage reﬁnement. These methods can only process limited information at one time, and they are not able to thoroughly exploit the abundant information in ELIs. Unlike previous methods, Ele-gantSeg can elegantly process holistic ELISS by extending the tensor storage from GPU memory to host memory. To the best of our knowledge, it is the ﬁrst time that ELISS can be performed holistically. Besides, ElegantSeg is speciﬁ-cally designed with three modules to utilize the characteristics of ELIs, including the multiple large kernel module for developing long-range dependency, the efﬁcient class relation module for building holistic contextual relationships, and the boundary-aware enhancement module for obtaining complete object boundaries. ElegantSeg outperforms previous state-of-the-art on two typical ELISS datasets. We hope that ElegantSeg can open a new perspective for ELISS. The code and models will be made publicly available.
Direct Evidence of Photochemistry in an Exoplanet Atmosphere

Photochemistry is a fundamental process of planetary atmospheres that is integral to habitability, atmospheric composition and stability, and aerosol formation [1]. However, no unambigu-ous photochemical products have been detected in exoplanet atmospheres to date. Here we show that photochemically produced sulphur dioxide (SO 2 ) is present in the atmosphere of the hot, giant exoplanet WASP-39b, as constrained by data from the JWST Transiting Exoplanet Early Release Science Program [2, 3] and informed by a suite of photochemical models. We ﬁnd that SO 2 is produced by successive oxidation of sulphur radicals freed when hydrogen sulphide (H 2 S) is destroyed. The SO 2 distribution computed by the photochemical models robustly explains the 4.05 µ m spectral feature seen in JWST transmission spectra [4] [Rustamkulov et al.(submitted), Alderson et al.(submitted)] and leads to observable features at ultraviolet and thermal infrared wavelengths not available from the current observations. The sensitivity of the SO 2 feature to the enrichment of heavy elements in the atmosphere (“metal-licity”) suggests that it can be used as a powerful tracer of atmospheric properties, with our results implying a metallicity of ∼ 10 × solar for WASP-39b. Through providing improved constraints on bulk metallicity and sulphur abundance, the detection of SO 2 opens a new avenue for the investigation of giant-planet formation. Our work demonstrates that sulphur photochemistry may be readily observable for exoplanets with super-solar metallicity and equilibrium temperatures (cid:38) 750 K. The conﬁrmation of photochemistry through the agreement between theoretical predictions and observational data is pivotal for further atmospheric characterisation studies. WASP-39b
The electrical properties of dislocations in semiconductors

Abstract Dislocations in semiconductors have been associated with a variety of novel phenomena: enhanced carrier recombination, poor electronic-device characteristics, degradation of semiconductor lasers, one-dimensional conductivity and gettering of impurities are but a few examples. In this review some of the basic physics governing the electronic properties of dislocations is qualitatively described. After a general introduction, the state of knowledge regarding the structure of dislocations, calculations regarding their band structure and experimental investigations of the electronic properties of dislocations are reviewed. Finally it is shown how the mechanical and electronic properties of dislocations are inextricably bound together. The article is concluded with a brief section on the future trends in this continuing field of research.
The LOFAR Tied-Array All-Sky Survey: Timing of 35 radio pulsars and an overview of the properties of the LOFAR pulsar discoveries

The LOFAR Tied-Array All-Sky Survey (LOTAAS) is the most sensitive untargeted radio pulsar survey performed at low radio frequencies (119--151\,MHz) to date and has discovered 76 new radio pulsars, among which the 23.5-s pulsar J0250+5854, up until recently the slowest-spinning radio pulsar known. Here, we report on the timing solutions of 35 pulsars discovered by LOTAAS, which include a nulling pulsar and a mildly recycled pulsar, and thereby complete the full timing analysis of the LOTAAS pulsar discoveries. We give an overview of the findings from the full LOTAAS sample of 76 pulsars, discussing their pulse profiles, radio spectra and timing parameters. We found that the pulse profiles of some of the pulsars show profile variations in time or frequency and while some pulsars show signs of scattering, a large majority display no pulse broadening. The LOTAAS discoveries have on average steeper radio spectra and have longer spin periods ($1.4\times$) as well as lower spin-down rates ($3.1\times$) compared to the known pulsar population. We discuss the cause of these differences, and attribute them to a combination of selection effects of the LOTAAS survey as well as previous pulsar surveys, though can not rule out that older pulsars tend to have steeper radio spectra.
Towards Greener Solutions for Steering Angle Prediction

In this paper, we investigate the two most popular families of deep neural architectures (i.e., ResNets and Inception nets) for the autonomous driving task of steering angle prediction. This work provides preliminary evidence that Inception architectures can perform as well or better than ResNet architectures with less complexity for the autonomous driving task. Primary motivation includes support for further research in smaller, more eﬃcient neural network architectures such that can not only accomplish complex tasks, such as steering angle predictions, but also produce less carbon emissions, or, more succinctly, neural networks that are more environmentally friendly. We look at various sizes of ResNet and InceptionNet models to compare results. Our derived models can achieve state-of-the-art results in terms of steering angle MSE.
L-MAE: Masked Autoencoders are Semantic Segmentation Datasets Augmenter

Generating semantic segmentation datasets has consistently been laborious and time-consuming, particularly in the context of large models or specialized domains(i.e. Medical Imaging or Remote Sensing). Specifically, large models necessitate a substantial volume of data, while datasets in professional domains frequently require the involvement of domain experts. Both scenarios are susceptible to inaccurate data labeling, which can significantly affect the ultimate performance of the trained model. This paper proposes a simple and effective label pixel-level completion method, \textbf{Label Mask AutoEncoder} (L-MAE), which fully uses the existing information in the label to generate the complete label. The proposed model are the first to apply the Mask Auto-Encoder to downstream tasks. In detail, L-MAE adopts the fusion strategy that stacks the label and the corresponding image, namely fuse map. Moreover, since some of the image information is lost when masking the fuse map, direct reconstruction may lead to poor performance. We proposed Image Patch Supplement algorithm to supplement the missing information during the mask-reconstruct process, and empirically found that an average of 4.1\% mIoU can be improved. We conducted a experiment to evaluate the efficacy of L-MAE to complete the dataset. We employed a degraded Pascal VOC dataset and the degraded dataset enhanced by L-MAE to train an identical conventional semantic segmentation model for the initial set of experiments. The results of these experiments demonstrate a performance enhancement of 13.5\% in the model trained with the L-MAE-enhanced dataset compared to the unenhanced dataset.
ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields

We present ESLAM, an efficient implicit neural representation method for Simultaneous Localization and Mapping (SLAM). ESLAM reads RGB-D frames with unknown camera poses in a sequential manner and incrementally reconstructs the scene representation while estimating the current camera position in the scene. We incorporate the latest advances in Neural Radiance Fields (NeRF) into a SLAM system, resulting in an efficient and accurate dense visual SLAM method. Our scene representation consists of multi-scale axis-aligned perpendicular feature planes and shallow decoders that, for each point in the continuous space, decode the interpolated features into Truncated Signed Distance Field (TSDF) and RGB values. Our extensive experiments on three standard datasets, Replica, ScanNet, and TUM RGB-D show that ESLAM improves the accuracy of 3D reconstruction and camera localization of state-of-the-art dense visual SLAM methods by more than 50%, while it runs up to ×10 faster and does not require any pre-training. Project page: https://www.idiap. ch/paper/eslam.
TIER-A: Denoising Learning Framework for Information Extraction

With the development of deep neural language models, great progress has been made in information extraction recently. However, deep learning models often overfit on noisy data points, leading to poor performance. In this work, we examine the role of information entropy in the overfitting process and draw a key insight that overfitting is a process of overconfidence and entropy decreasing. Motivated by such properties, we propose a simple yet effective co-regularization joint-training framework TIER-A, Aggregation Joint-training Framework with Temperature Calibration and Information Entropy Regularization. Our framework consists of several neural models with identical structures. These models are jointly trained and we avoid overfitting by introducing temperature and information entropy regularization. Extensive experiments on two widely-used but noisy datasets, TACRED and CoNLL03, demonstrate the correctness of our assumption and the effectiveness of our framework.
Light and Heavy Element Abundance Variations in the Outer Halo Globular Cluster NGC 6229

NGC 6229 is a relatively massive outer halo globular cluster that is primarily known for exhibiting a peculiar bimodal horizontal branch morphology. Given the paucity of spectroscopic data on this cluster, we present a detailed chemical composition analysis of 11 red giant branch members based on high resolution (R ≈ 38,000), high S/N (>100) spectra obtained with the MMT-Hectochelle instrument. We find the cluster to have a mean heliocentric radial velocity of , a small dispersion of , and a relatively low . The cluster is moderately metal-poor with dex and a modest dispersion of 0.06 dex. However, 18% (2/11) of the stars in our sample have strongly enhanced [La, Nd/Fe] ratios that are correlated with a small (∼0.05 dex) increase in [Fe/H]. NGC 6229 shares several chemical signatures with M75, NGC 1851, and the intermediate metallicity populations of ω Cen, which lead us to conclude that NGC 6229 is a lower mass iron-complex cluster. The light elements exhibit the classical (anti-)correlations that extend up to Si, but the cluster possesses a large gap in the O–Na plane that separates first and second generation stars. NGC 6229 also has unusually low [Na, Al/Fe] abundances that are consistent with an accretion origin. A comparison with M54 and other Sagittarius clusters suggests that NGC 6229 could also be the remnant core of a former dwarf spheroidal galaxy.
Rethinking Batch Sample Relationships for Data Representation: A Batch-Graph Transformer Based Approach

Exploring sample relationships within each mini-batch has shown great potential for learning image representations. Existing works generally adopt the regular Transformer to model the visual content relationships, ignoring the cues of semantic/label correlations between samples. Also, they generally adopt the ‘full’ self-attention mechanism which are obviously redundant and also sensitive to the noisy samples. To overcome these issues, in this paper, we design a simple yet flexible Batch-Graph Transformer (BGFormer) for mini-batch sample representations by deeply capturing the relationships of image samples from both visual and semantic perspectives. BGFormer has three main aspects. (1) It employs a flexible graph model, termed Batch Graph to jointly encode both visual and semantic relationships of samples within each mini-batch. (2) It explores the neighborhood relationships of samples by borrowing the idea of sparse graph representation which thus performs robustly, w.r. t. , noisy samples. (3) It devises a novel specific Transformer architecture that mainly adopts dual structure-constrained self-attention (SSA), together with graph normalization, FFN, etc, to carefully exploit the batch graph information for sample tokens (nodes) representations. As an application, we apply BGFormer to the metric learning tasks. Extensive experiments on four popular datasets demonstrate the effectiveness of the proposed model.
Curiosity in hindsight

Consider the problem of exploration in sparse-reward or reward-free environments, such as in Montezuma's Revenge. In the curiosity-driven paradigm, the agent is rewarded for how much each realized outcome differs from their predicted outcome. But using predictive error as intrinsic motivation is fragile in stochastic environments, as the agent may become trapped by high-entropy areas of the state-action space, such as a"noisy TV". In this work, we study a natural solution derived from structural causal models of the world: Our key idea is to learn representations of the future that capture precisely the unpredictable aspects of each outcome -- which we use as additional input for predictions, such that intrinsic rewards only reflect the predictable aspects of world dynamics. First, we propose incorporating such hindsight representations into models to disentangle"noise"from"novelty", yielding Curiosity in Hindsight: a simple and scalable generalization of curiosity that is robust to stochasticity. Second, we instantiate this framework for the recently introduced BYOL-Explore algorithm as our prime example, resulting in the noise-robust BYOL-Hindsight. Third, we illustrate its behavior under a variety of different stochasticities in a grid world, and find improvements over BYOL-Explore in hard-exploration Atari games with sticky actions. Notably, we show state-of-the-art results in exploring Montezuma's Revenge with sticky actions, while preserving performance in the non-sticky setting.