Miracle-dz/UniFAR-FTUs huggingface dataset:
CelebV-HQ: A Large-Scale Video Facial Attributes Dataset

Large-scale datasets have played indispensable roles in the recent success of face generation/editing and significantly facilitated the advances of emerging research fields. However, the academic community still lacks a video dataset with diverse facial attribute annotations, which is crucial for the research on face-related videos. In this work, we propose a large-scale, high-quality, and diverse video dataset with rich facial attribute annotations, named the High-Quality Celebrity Video Dataset (CelebV-HQ). CelebV-HQ contains 35,666 video clips with the resolution of 512x512 at least, involving 15,653 identities. All clips are labeled manually with 83 facial attributes, covering appearance, action, and emotion. We conduct a comprehensive analysis in terms of age, ethnicity, brightness stability, motion smoothness, head pose diversity, and data quality to demonstrate the diversity and temporal coherence of CelebV-HQ. Besides, its versatility and potential are validated on two representative tasks, i.e., unconditional video generation and video facial attribute editing. Furthermore, we envision the future potential of CelebV-HQ, as well as the new opportunities and challenges it would bring to related research directions. Data, code, and models are publicly available. Project page: https://celebv-hq.github. io.
Maximal displacement of spectrally negative branching Lévy processes

We consider a branching Markov process in continuous time in which the particles evolve independently as spectrally negative L\'evy processes. When the branching mechanism is critical or subcritical, the process will eventually die and we may define its overall maximum, i.e. the maximum location ever reached by a particule. The purpose of this paper is to give asymptotic estimates for the survival function of this maximum. In particular, we show that in the critical case the asymptotics is polynomial when the underlying L\'evy process oscillates or drifts towards $+\infty$, and is exponential when it drifts towards $-\infty$.
MemSAC: Memory Augmented Sample Consistency for Large Scale Domain Adaptation

Practical real world datasets with plentiful categories introduce new challenges for unsupervised domain adaptation like small inter-class discriminability, that existing approaches relying on domain invariance alone cannot handle sufficiently well. In this work we propose MemSAC, which exploits sample level similarity across source and target domains to achieve discriminative transfer, along with architectures that scale to a large number of categories. For this purpose, we first introduce a memory augmented approach to efficiently extract pairwise similarity relations between labeled source and unlabeled target domain instances, suited to handle an arbitrary number of classes. Next, we propose and theoretically justify a novel variant of the contrastive loss to promote local consistency among within-class cross domain samples while enforcing separation between classes, thus preserving discriminative transfer from source to target. We validate the advantages of MemSAC with significant improvements over previous state-of-the-art on multiple challenging transfer tasks designed for large-scale adaptation, such as DomainNet with 345 classes and fine-grained adaptation on Caltech-UCSD birds dataset with 200 classes. We also provide in-depth analysis and insights into the effectiveness of MemSAC.
No more fine-tuning? an experimental evaluation of prompt tuning in code intelligence

Pre-trained models have been shown effective in many code intelligence tasks. These models are pre-trained on large-scale unlabeled corpus and then fine-tuned in downstream tasks. However, as the inputs to pre-training and downstream tasks are in different forms, it is hard to fully explore the knowledge of pre-trained models. Besides, the performance of fine-tuning strongly relies on the amount of downstream data, while in practice, the scenarios with scarce data are common. Recent studies in the natural language processing (NLP) field show that prompt tuning, a new paradigm for tuning, alleviates the above issues and achieves promising results in various NLP tasks. In prompt tuning, the prompts inserted during tuning provide task-specific knowledge, which is especially beneficial for tasks with relatively scarce data. In this paper, we empirically evaluate the usage and effect of prompt tuning in code intelligence tasks. We conduct prompt tuning on popular pre-trained models CodeBERT and CodeT5 and experiment with three code intelligence tasks including defect prediction, code summarization, and code translation. Our experimental results show that prompt tuning consistently outperforms fine-tuning in all three tasks. In addition, prompt tuning shows great potential in low-resource scenarios, e.g., improving the BLEU scores of fine-tuning by more than 26% on average for code summarization. Our results suggest that instead of fine-tuning, we could adapt prompt tuning for code intelligence tasks to achieve better performance, especially when lacking task-specific data.
A toolbox for black hole scattering

Hawking's free field theory is expected to break down after Page time. In previous work, we have shown that a primary dynamical reason for this breakdown is the dominance of graviton fluctuations of the horizon that mediate scattering processes. In this article, we present a toolbox for such `black hole scattering' computations. The toolbox comprises of explicit expressions for the graviton propagator near the horizon in an angular momentum basis for all angular momentum modes of either parity, the leading interaction rules, and most importantly a rewriting of the theory in terms of a scalar theory with an interesting four-vertex. We demonstrate how this rewriting drastically reduces the number of diagrams to be calculated in the original formulation. Finally and perhaps most remarkably, we observe that the black hole entropy appears to emerge from the multiplicity of external legs of the dominant $2\rightarrow2N$ amplitudes in this theory.
The FASER Detector

FASER, the ForwArd Search ExpeRiment, is an experiment dedicated to searching for light, extremely weakly-interacting particles at CERN’s Large Hadron Collider (LHC). Such particles may be produced in the very forward direction of the LHC’s high-energy collisions and then decay to visible particles inside the FASER detector, which is placed 480 m downstream of the ATLAS interaction point, aligned with the beam collisions axis. FASER also includes a sub-detector, FASERν, designed to detect neutrinos produced in the LHC collisions and to study their properties. In this paper, each component of the FASER detector is described in detail, as well as the installation of the experiment system and its commissioning using cosmic-rays collected in September 2021 and during the LHC pilot beam test carried out in October 2021. FASER will start taking LHC collision data in 2022, and will run throughout LHC Run 3.
Density-Aware Personalized Training for Risk Prediction in Imbalanced Medical Data

Medical events of interest, such as mortality, often happen at a low rate in electronic medical records, as most admitted patients survive. Training models with this imbalance rate (class density discrepancy) may lead to suboptimal prediction. Traditionally this problem is addressed through ad-hoc methods such as resampling or reweighting but performance in many cases is still limited. We propose a framework for training models for this imbalance issue: 1) we first decouple the feature extraction and classification process, adjusting training batches separately for each component to mitigate bias caused by class density discrepancy; 2) we train the network with both a density-aware loss and a learnable cost matrix for misclassifications. We demonstrate our model's improved performance in real-world medical datasets (TOPCAT and MIMIC-III) to show improved AUC-ROC, AUC-PRC, Brier Skill Score compared with the baselines in the domain.
Progressive Scene Text Erasing with Self-Supervision

Scene text erasing seeks to erase text contents from scene images and current state-of-the-art text erasing models are trained on large-scale synthetic data. Although data synthetic engines can provide vast amounts of annotated training samples, there are differences between synthetic and real-world data. In this paper, we employ self-supervision for feature representation on unlabeled real-world scene text images. A novel pretext task is designed to keep consistent among text stroke masks of image variants. We design the Progressive Erasing Network in order to remove residual texts. The scene text is erased progressively by leveraging the intermediate generated results which provide the foundation for subsequent higher quality results. Experiments show that our method significantly improves the generalization of the text erasing task and achieves state-of-the-art performance on public benchmarks.
Marginal Bayesian Statistics Using Masked Autoregressive Flows and Kernel Density Estimators with Examples in Cosmology

Cosmological experiments often employ Bayesian workflows to derive constraints on cosmological and astrophysical parameters from their data. It has been shown that these constraints can be combined across different probes such as Planck and the Dark Energy Survey and that this can be a valuable exercise to improve our understanding of the universe and quantify tension between multiple experiments. However, these experiments are typically plagued by differing systematics, instrumental effects and contaminating signals, which we collectively refer to as `nuisance' components, that have to be modelled alongside target signals of interest. This leads to high dimensional parameter spaces, especially when combining data sets, with>20 dimensions of which only around 5 correspond to key physical quantities. We present a means by which to combine constraints from different data sets in a computationally efficient manner by generating rapid, reusable and reliable marginal probability density estimators, giving us access to nuisance-free likelihoods. This is possible through the unique combination of nested sampling, which gives us access to Bayesian evidences, and the marginal Bayesian statistics code MARGARINE. Our method is lossless in the signal parameters, resulting in the same posterior distributions as would be found from a full nested sampling run over all nuisance parameters, and typically quicker than evaluating full likelihoods. We demonstrate our approach by applying it to the combination of posteriors from the Dark Energy Survey and Planck.
The Physical Conditions of Emission-line Galaxies at Cosmic Dawn from JWST/NIRSpec Spectroscopy in the SMACS 0723 Early Release Observations

We present rest-frame optical emission-line flux ratio measurements for five z > 5 galaxies observed by the James Webb Space Telescope Near-Infared Spectrograph (NIRSpec) in the SMACS 0723 Early Release Observations. We add several quality-control and post-processing steps to the NIRSpec pipeline reduction products in order to ensure reliable relative flux calibration of emission lines that are closely separated in wavelength, despite the uncertain absolute spectrophotometry of the current version of the reductions. Compared to z ∼ 3 galaxies in the literature, the z > 5 galaxies have similar [O iii]λ5008/Hβ ratios, similar [O iii]λ4364/Hγ ratios, and higher (∼0.5 dex) [Ne III]λ3870/[O II]λ3728 ratios. We compare the observations to MAPPINGS V photoionization models and find that the measured [Ne III]λ3870/[O II]λ3728, [O iii]λ4364/Hγ, and [O iii]λ5008/Hβ emission-line ratios are consistent with an interstellar medium (ISM) that has very high ionization ( log(Q)≃8−9 , units of cm s−1), low metallicity (Z/Z ⊙ ≲ 0.2), and very high pressure ( log(P/k)≃8−9 , units of cm−3). The combination of [O iii]λ4364/Hγ and [O iii]λ(4960 + 5008)/Hβ line ratios indicate very high electron temperatures of 4.1<log(Te/K)<4.4 , further implying metallicities of Z/Z ⊙ ≲ 0.2 with the application of low-redshift calibrations for “T e -based” metallicities. These observations represent a tantalizing new view of the physical conditions of the ISM in galaxies at cosmic dawn.
Bistability of the Atmospheric Circulation on TRAPPIST-1e

Using a 3D general circulation model, we demonstrate that a confirmed rocky exoplanet and a primary observational target, TRAPPIST-1e presents an interesting case of climate bistability. We find that the atmospheric circulation on TRAPPIST-1e can exist in two distinct regimes for a 1 bar nitrogen-dominated atmosphere. One is characterized by a single strong equatorial prograde jet and a large day–night temperature difference; the other is characterized by a pair of mid-latitude prograde jets and a relatively small day–night contrast. The circulation regime appears to be highly sensitive to the model setup, including initial and surface boundary conditions, as well as physical parameterizations of convection and cloud radiative effects. We focus on the emergence of the atmospheric circulation during the early stages of simulations and show that the regime bistability is associated with a delicate balance between the zonally asymmetric heating, mean overturning circulation, and mid-latitude baroclinic instability. The relative strength of these processes places the GCM simulations on different branches of the evolution of atmospheric dynamics. The resulting steady states of the two regimes have consistent differences in the amount of water content and clouds, affecting the water absorption bands as well as the continuum level in the transmission spectrum, although they are too small to be detected with current technology. Nevertheless, this regime bistability affects the surface temperature, especially on the night side of the planet, and presents an interesting case for understanding atmospheric dynamics and highlights uncertainty in 3D GCM results, motivating more multimodel studies.
The evolution of the galaxy UV luminosity function at redshifts z ≃ 8 – 15 from deep JWST and ground-based near-infrared imaging

We reduce and analyse the available JWST ERO and ERS NIRCam imaging (SMACS0723, GLASS, CEERS) in combination with the latest deep ground-based near-infrared imaging in the COSMOS field (provided by UltraVISTA DR5) to produce a new measurement of the evolving galaxy UV luminosity function (LF) over the redshift range z = 8 − 15. This yields a new estimate of the evolution of UV luminosity density (ρUV), and hence cosmic star formation rate density (ρSFR) out to within <300 Myr of the Big Bang. Our results confirm that the high-redshift LF is best described by a double power law (rather than a Schechter) function up to z ∼ 10, and that the LF and the resulting derived ρUV (and thus ρSFR), continues to decline gradually and steadily up to z ∼ 15 (as anticipated from previous studies which analysed the pre-existing data in a consistent manner to this study). We provide details of the 61 high-redshift galaxy candidates, 47 of which are new, that have enabled this new analysis. Our sample contains 6 galaxies at z ≥ 12, one of which appears to set a new redshift record as an apparently robust galaxy candidate at z ≃ 16.4, the properties of which we therefore consider in detail. The advances presented here emphasize the importance of achieving high dynamic range in studies of early galaxy evolution, and re-affirm the enormous potential of forthcoming larger JWST programmes to transform our understanding of the young Universe.
A T‐Matrix Based Approach to Homogenize Artificial Materials

The accurate and efficient computation of the electromagnetic response of objects made from artificial materials is crucial for designing photonic functionalities and interpreting experiments. Advanced fabrication techniques can nowadays produce new materials as 3D lattices of scattering unit cells. Computing the response of objects of arbitrary shape made from such materials is typically computationally prohibitive unless an effective homogeneous medium approximates the discrete material. In here, a homogenization method based on the effective transition (T‐)matrix, Teff${T_{eff}}$ is introduced. Such a matrix captures the exact response of the discrete material, is determined by the T‐matrix of the isolated unit cell and the material lattice vectors, and is free of spatial dispersion. The truncation of Teff${T_{eff}}$ to dipolar order determines the common bi‐anisotropic constitutive relations. When combined with quantum‐chemical and Maxwell solvers, the method allows one to compute the response of arbitrarily‐shaped volumetric patchworks of structured molecular materials and metamaterials.
Rate-Splitting Multiple Access in Multi-Cell Dense Networks: A Stochastic Geometry Approach

In this article, the potential benefits of applying the Rate-Splitting Multiple Access (RSMA) in multi-cell dense networks are explored. Using tools of stochastic geometry, the sum-rate of RSMA-enhanced multi-cell dense networks is evaluated mathematically based on a Moment Generating Function (MGF) based framework to prove that RSMA is a general and powerful strategy for multi-antenna downlink systems. Further elaboration of the systematic performance metrics is undertaken by developing analytical expressions for area spectral efficiency and sum-rate in the RSMA-enhanced multi-cell dense networks. Based on the tractable expressions, we then offer an optimization framework for energy efficiency in terms of the number of antennas. Additionally, simulation results are shown to verify the accuracy of our analytical results and provide some insightful insights into system design. Analytically, it has been shown that: 1) the sum-rate of RSMA-enhanced multi-cell dense networks is significantly influenced by the power splitting ratio, and there is a unique value that maximizes the sum-rate; 2) the RSMA-enhanced multi-cell dense networks transmission scheme has superior sum-rate performance compared with Non-Orthogonal Multiple Access (NOMA) and Space-Division Multiple Access (SDMA) in a wide range of power splitting ratio; 3) By increasing the number of antennas and BS density in an RSMA-enhanced multi-cell dense network, the area spectral efficiency can be substantially enhanced; 4) As for energy efficiency, there exists an optimal antenna number for maximizing this performance metric.
Active Learning Strategies for Weakly-supervised Object Detection

Object detectors trained with weak annotations are affordable alternatives to fully-supervised counterparts. However, there is still a significant performance gap between them. We propose to narrow this gap by fine-tuning a base pre-trained weakly-supervised detector with a few fully-annotated samples automatically selected from the training set using ``box-in-box'' (BiB), a novel active learning strategy designed specifically to address the well-documented failure modes of weakly-supervised detectors. Experiments on the VOC07 and COCO benchmarks show that BiB outperforms other active learning techniques and significantly improves the base weakly-supervised detector's performance with only a few fully-annotated images per class. BiB reaches 97% of the performance of fully-supervised Fast RCNN with only 10% of fully-annotated images on VOC07. On COCO, using on average 10 fully-annotated images per class, or equivalently 1% of the training set, BiB also reduces the performance gap (in AP) between the weakly-supervised detector and the fully-supervised Fast RCNN by over 70%, showing a good trade-off between performance and data efficiency. Our code is publicly available at https://github.com/huyvvo/BiB.
Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference

We present a non-asymptotic lower bound on the eigenspectrum of the design matrix generated by any linear bandit algorithm with sub-linear regret when the action set has well-behaved curvature. Specifically, we show that the minimum eigenvalue of the expected design matrix grows as $\Omega(\sqrt{n})$ whenever the expected cumulative regret of the algorithm is $O(\sqrt{n})$, where $n$ is the learning horizon, and the action-space has a constant Hessian around the optimal arm. This shows that such action-spaces force a polynomial lower bound rather than a logarithmic lower bound, as shown by \cite{lattimore2017end}, in discrete (i.e., well-separated) action spaces. Furthermore, while the previous result is shown to hold only in the asymptotic regime (as $n \to \infty$), our result for these"locally rich"action spaces is any-time. Additionally, under a mild technical assumption, we obtain a similar lower bound on the minimum eigen value holding with high probability. We apply our result to two practical scenarios -- \emph{model selection} and \emph{clustering} in linear bandits. For model selection, we show that an epoch-based linear bandit algorithm adapts to the true model complexity at a rate exponential in the number of epochs, by virtue of our novel spectral bound. For clustering, we consider a multi agent framework where we show, by leveraging the spectral result, that no forced exploration is necessary -- the agents can run a linear bandit algorithm and estimate their underlying parameters at once, and hence incur a low regret.
Joint ergodicity for commuting transformations and applications to polynomial sequences

We give necessary and sufficient conditions for joint ergodicity results of collections of sequences with respect to systems of commuting measure preserving transformations. Combining these results with a new technique that we call"seminorm smoothening", we settle several conjectures related to multiple ergodic averages of commuting transformations with polynomial iterates. We show that the Host-Kra factor is characteristic for pairwise independent polynomials, and that under certain ergodicity conditions the associated ergodic averages converge to the product of integrals. Moreover, when the polynomials are linearly independent, we show that the rational Kronecker factor is characteristic and deduce Khintchine-type lower bounds for the related multiple recurrence problem. Finally, we prove a nil plus null decomposition result for multiple correlation sequences of commuting transformations in the case where the iterates are given by families of pairwise independent polynomials.
Error-Aware Spatial Ensembles for Video Frame Interpolation

Video frame interpolation (VFI) algorithms have im-proved considerably in recent years due to unprecedented progress in both data-driven algorithms and their imple-mentations. Recent research has introduced advanced motion estimation or novel warping methods as the means to address challenging VFI scenarios. However, none of the published VFI works considers the spatially non-uniform characteristics of the interpolation error (IE). This work introduces such a solution. By closely examining the correlation between optical ﬂow and IE, the paper proposes novel error prediction metrics that partition the middle frame into distinct regions corresponding to different IE levels. Building upon this IE-driven segmentation, and through the use of novel error-controlled loss functions, it introduces an ensemble of spatially adaptive interpolation units that progressively processes and integrates the segmented regions. This spatial ensemble results in an effective and computa-tionally attractive VFI solution. Extensive experimentation on popular video interpolation benchmarks indicates that the proposed solution outperforms the current state-of-the-art (SOTA) in applications of current interest.