Miracle-dz/UniFAR-FTUs huggingface dataset:
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens

Recent action recognition models have achieved impressive results by integrating objects, their locations and interactions. However, obtaining dense structured annotations for each frame is tedious and time-consuming, making these methods expensive to train and less scalable. At the same time, if a small set of annotated images is available, either within or outside the domain of interest, how could we leverage these for a video downstream task? We propose a learning framework StructureViT (SViT for short), which demonstrates how utilizing the structure of a small number of images only available during training can improve a video model. SViT relies on two key insights. First, as both images and videos contain structured information, we enrich a transformer model with a set of object tokens that can be used across images and videos. Second, the scene representations of individual frames in video should “align” with those of still images. This is achieved via a Frame-Clip Consistency loss, which ensures the ﬂow of structured information between images and videos. We explore a particular instantiation of scene structure, namely a Hand-Object Graph , consisting of hands and objects with their locations as nodes, and physical relations of contact/no-contact as edges. SViT shows strong performance improvements on multiple video understanding tasks and datasets. Furthermore, it won in the Ego4D CVPR’22 Object State Localization challenge. For code and pretrained models, visit the project page at https://eladb3.github. io/SViT/
Oxygen and calcium nebular emission line relationships in core-collapse supernovae and Ca-rich transients

This work examines the relationships between the properties (flux ratios, full width at half-maximum velocities) of the [O i] λλ6300, 6364, [Ca ii] λλ7291, 7323, and the Ca ii near-infrared triplet, emission lines of a large sample of core-collapse supernovas (SNe) and Ca-rich transients (509 spectra of 86 transients, of which 10 transients are Ca-rich events). Line-flux ratios as a function of time were investigated with differences identified between the transient classes, in particular the Type II SNe were found to have distinct line-flux ratios compared to stripped-envelope (SE) SNe. No correlation was found between the [Ca ii]/[O i] flux ratios of SE-SNe and their ejecta masses and kinetic energies (as measured from light curve modelling), suggesting that there may be a contribution from an additional power source in more luminous SE-SNe. We found that the mean characteristic width of the [Ca ii] emission line is less than the [O i] emission line for all SN types, indicating that the [Ca ii] emission typically originates from deeper in the ejecta than [O i]. This is in some tension with standard models for emission in Type II SNe. The emission line properties of Type II SNe were also compared to theoretical models and found to favour lower mass tracks (MZAMS< 15 M⊙), with no evidence found for significant mixing of 56Ni into the H envelope nor Ca mixed into the O shell. The flux ratios of some superluminous SNe were found to be similar to those of SE-SNe when scaling to account for their longer rise times was applied (although we caution the sample size is small).
FisheyeEX: Polar Outpainting for Extending the FoV of Fisheye Lens

Fisheye lens gains increasing applications in computational photography and assisted driving because of its wide field of view (FoV). However, the fisheye image generally contains invalid black regions induced by its imaging model. In this paper, we present a FisheyeEX method that extends the FoV of the fisheye lens by outpainting the invalid regions, improving the integrity of captured scenes. Compared with the rectangle and undistorted image, there are two challenges for fisheye image outpainting: irregular painting regions and distortion synthesis. Observing the radial symmetry of the fisheye image, we first propose a polar outpainting strategy to extrapolate the coherent semantics from the center to the outside region. Such an outpainting manner considers the distribution pattern of radial distortion and the circle boundary, boosting a more reasonable completion direction. For the distortion synthesis, we propose a spiral distortion-aware perception module, in which the learning path keeps consistent with the distortion prior of the fisheye image. Subsequently, a scene revision module rearranges the generated pixels with the estimated distortion to match the fisheye image, thus extending the FoV. In the experiment, we evaluate the proposed FisheyeEX on three popular outdoor datasets: Cityscapes, BDD100k, and KITTI, and one real-world fisheye image dataset. The results demonstrate that our approach significantly outperforms the state-of-the-art methods, gaining around 27% more content beyond the original fisheye image.
The tunneling wavefunction in Kantowski-Sachs quantum cosmology

We use a path-integral approach to study the tunneling wave function in quantum cosmology with spatial topology S 1 × S 2 and positive cosmological constant (the Kantowski-Sachs model). If the initial scale factors of both S 1 and S 2 are set equal to zero, the wave function describes (semiclassically) a universe originating at a singularity. This may be interpreted as indicating that an S 1 × S 2 universe cannot nucleate out of nothing in a non-singular way. Here we explore an alternative suggestion by Halliwell and Louko that creation from nothing corresponds in this model to setting the initial volume to zero. We find that the only acceptable version of this proposal is to fix the radius of S 1 to zero, supplementing this with the condition of smooth closure (absence of a conical singularity). The resulting wave function predicts an inflating universe of high anisotropy, which however becomes locally isotropic at late times. Unlike the de Sitter model, the total nucleation probability is not exponentially suppressed, unless a Gauss-Bonnet term is added to the action.
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games

Algorithms designed for single-agent reinforcement learning (RL) generally fail to converge to equilibria in two-player zero-sum (2p0s) games. On the other hand, game-theoretic algorithms for approximating Nash and regularized equilibria in 2p0s games are not typically competitive for RL and can be difﬁcult to scale. As a result, algorithms for these two cases are generally developed and evaluated separately. In this work, we show that a single algorithm can produce strong results in both settings, despite their fundamental differences. This algorithm, which we call magnet mirror descent (MMD), is a simple extension to mirror descent and a special case of a non-Euclidean proximal gradient algorithm. From a theoretical standpoint, we prove a novel linear convergence for this non-Euclidean proximal gradient algorithm for a class of variational inequality problems. It follows from this result that MMD converges linearly to quantal response equilibria (i.e., entropy regularized Nash equilibria) in extensive-form games; this is the ﬁrst time linear convergence has been proven for a ﬁrst order solver. Moreover, applied as a tabular Nash equilibrium solver via self-play, we show empirically that MMD produces results competitive with CFR; this
A Graph Transformation Strategy for Optimizing SpTRSV

Sparse triangular solve (SpTRSV) is an extensively studied computational kernel. An important obstacle in parallel SpTRSV implementations is that in some parts of a sparse matrix the computation is serial. By transforming the dependency graph, it is possible to increase the parallelism of the parts that lack it. In this work, we present an approach to increase the parallelism degree of a sparse matrix, discuss its limitations and possible improvements, and we compare it to a previous manual approach. The results provide several hints on how to craft a collection of strategies to transform a dependency graph.
Gaia Data Release 3. Rotational modulation and patterns of color variations in solar-like variables

The Gaia third Data Release (DR3) presents a catalogue of 474\,026 stars with variability induced by magnetic activity. For each star, the catalogue provides a list of about 70 parameters among which the most important are the stellar rotation period $P$, the photometric amplitude $A$ of the rotational signal and the Pearson Correlation Coefficient $r_0$ between brightness and magnitude variations. The Specific Objects Study (SOS) pipeline, developed to characterise magnetically active stars with Gaia Data, has been described in the paper accompanying the Gaia second Data Release. Here we describe the changes made to the pipeline and a new method developed to analyze Gaia time-series and to reveal spurious signals induced by instrumental effects or by the peculiar nature of the investigated stellar source. The period-amplitude diagram obtained with the DR3 data confirms the bimodal distribution of fast rotating stars seen in the DR2 release. The DR3 data permitted, for the first time, to analyze the patterns of magnitude-color variations for thousands of magnetically active stars. The measured $r_0$ values are tightly correlated with the stars position in the period-amplitude diagram. The relationship between the $P$, $A$ and $r_0$ parameters inferred for thousands of stars could be very useful to improve the understanding of stellar magnetic fields and to improve theoretical models. The method developed to reveal the spurious signals can be applied to each of the released Gaia photometric time-series and can be exploited by anyone interested in working directly with Gaia time-series.
Optimal Solutions for Joint Beamforming and Antenna Selection: From Branch and Bound to Machine Learning

—This work revisits the joint beamforming (BF) and antenna selection (AS) problem, as well as its robust beamforming (RBF) version under imperfect channel state information (CSI). Such problems arise in scenarios where the number of the radio frequency (RF) chains is smaller than that of the antenna elements at the transmitter, which has become a critical consideration in the era of large-scale arrays. The joint (R)BF&AS problem is a mixed integer and nonlinear program, and thus ﬁnding optimal solutions is often costly, if not outright impossible. The vast majority of the prior works tackled these problems using continuous optimization-based approximations—yet these approximations do not ensure optimality or even feasibility of the solutions. The main contribution of this work is threefold. First, an effective branch and bound (B&B) framework for solving the problems of interest is proposed. Leveraging existing BF and RBF solvers, it is shown that the B&B framework guarantees global optimality of the considered problems. Second, to expedite the potentially costly B&B algorithm, a machine learning (ML)-based scheme is proposed to help skip intermediate states of the B&B search tree. The learning model features a graph neural network (GNN)-based design that is resilient to a commonly encountered challenge in wireless communications, namely, the change of problem size (e.g., the number of users) across the training and test stages. Third, comprehensive performance characterizations are presented, showing that the GNN-based method retains the global optimality of B&B with provably reduced complexity, under reasonable conditions. Numerical simulations also show that the ML-based acceleration can often achieve an order-of-magnitude speedup relative to B&B.
iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects

Causal representation learning is the task of identifying the underlying causal variables and their relations from high-dimensional observations, such as images. Recent work has shown that one can reconstruct the causal variables from temporal sequences of observations under the assumption that there are no instantaneous causal relations between them. In practical applications, however, our measurement or frame rate might be slower than many of the causal effects. This effectively creates “instantaneous” effects and invalidates previous identiﬁability results. To address this issue, we propose iCITRIS, a causal representation learning method that can handle instantaneous effects in temporal sequences when given perfect interventions with known intervention targets. iCITRIS identiﬁes the causal factors from temporal observations, while simultaneously using a differentiable causal discovery method to learn their causal graph. In experiments on three video datasets, iCITRIS accurately identiﬁes the causal factors and their causal graph.
A Semi-Empirical Approach to a Physically Based Aging Model for Home Energy Management Systems

A growing interest in the study of aging related phenomena in lithium-ion batteries is propelled by the increasing utilization of energy storage systems in electric vehicles and in buildings as stationery energy accumulators paired with renewable energy sources. This paper proposes a mixed-degradation model approach that combines the benefits of a semi-empirical approach with that of a physics-based model. This enables easy calibration for different battery chemistries, the ability to extrapolate when necessary, and is computationally efficient enough to be coupled with real-time running control systems. To demonstrate the effectiveness of the proposed approach, the effect of two different control strategies in a smart home energy management system is demonstrated on the aging of a Lithium iron phosphate (LFP) battery.
Convergence and Stability of Coupled Belief-Strategy Learning Dynamics in Continuous Games

We propose a learning dynamics to model how strategic agents repeatedly play a continuous game while relying on an information platform to learn an unknown payoff-relevant parameter. In each time step, the platform updates a belief estimate of the parameter based on players’ strategies and realized payoffs using Bayes’ rule. Then, players adopt a generic learning rule to adjust their strategies based on the updated belief. We present results on the convergence of beliefs and strategies and the properties of convergent fixed points of the dynamics. We obtain sufficient and necessary conditions for the existence of globally stable fixed points. We also provide sufficient conditions for the local stability of fixed points. These results provide an approach to analyzing the long-term outcomes that arise from the interplay between Bayesian belief learning and strategy learning in games and enable us to characterize conditions under which learning leads to a complete information equilibrium. Funding: Financial support from the Air Force Office of Scientific Research [Project Building Attack Resilience into Complex Networks], the Simons Institute [research fellowship], and a Michael Hammer Fellowship is gratefully acknowledged.
Model-based Offline Imitation Learning with Non-expert Data

Although Behavioral Cloning (BC) in theory suffers compounding errors, its scalability and simplicity still makes it an attractive imitation learning algorithm. In contrast, imitation approaches with adversarial training typically does not share the same problem, but necessitates interactions with the environment. Meanwhile, most imitation learning methods only utilises optimal datasets, which could be significantly more expensive to obtain than its suboptimal counterpart. A question that arises is, can we utilise the suboptimal dataset in a principled manner, which otherwise would have been idle? We propose a scalable model-based offline imitation learning algorithmic framework that leverages datasets collected by both suboptimal and optimal policies, and show that its worst case suboptimality becomes linear in the time horizon with respect to the expert samples. We empirically validate our theoretical results and show that the proposed method \textit{always} outperforms BC in the low data regime on simulated continuous control domains
Some factors that affect ruminant meat quality: from the farm to the fork. Review

Cada vez mais os consumidores estao interessados na qualidade dos produtos os quais eles consomem; especialmente com referencia a carne. O consumo de carne de ruminantes e de seus derivados esta aumentando de forma muito rapida, especialmente em alguns paises como o Brasil, que e o principal exportador de carne do mundo. Este trabalho revisa diferentes fatores que afetam a qualidade da carne de ruminantes, sobretudo sobre as analises sensoriais. Alguns fatores ao longo da cadeia da carne sao analisados: aqueles que poderiam ser considerados pelos produtores relacionados ao melhoramento da qualidade dos seus produtos e aqueles relacionados aos habitos e comportamento dos consumidores. A maior parte dos artigos cientificos revisados foi desenvolvida pelo Grupo de Qualidade e Tecnologia de Carnes (Universidade de Zaragoza e CITA da regiao de Aragon na Espanha) isoladamente ou em colaboracao com grupos locais e internacionais.
INDIGO: Intrinsic Multimodality for Domain Generalization

For models to generalize under unseen domains (a.k.a domain generalization), it is crucial to learn feature representations that are domain-agnostic and capture the underlying semantics that makes up an object category. Recent advances towards weakly supervised vision-language models that learn holistic representations from cheap weakly supervised noisy text annotations have shown their ability on semantic understanding by capturing object characteristics that generalize under different domains. However, when multiple source domains are involved, the cost of curating textual annotations for every image in the dataset can blow up several times, depending on their number. This makes the process tedious and infeasible, hindering us from directly using these supervised vision-language approaches to achieve the best generalization on an unseen domain. Motivated from this, we study how multimodal information from existing pre-trained multimodal networks can be leveraged in an"intrinsic"way to make systems generalize under unseen domains. To this end, we propose IntriNsic multimodality for DomaIn GeneralizatiOn (INDIGO), a simple and elegant way of leveraging the intrinsic modality present in these pre-trained multimodal networks along with the visual modality to enhance generalization to unseen domains at test-time. We experiment on several Domain Generalization settings (ClosedDG, OpenDG, and Limited sources) and show state-of-the-art generalization performance on unseen domains. Further, we provide a thorough analysis to develop a holistic understanding of INDIGO.
TC-SfM: Robust Track-Community-Based Structure-From-Motion

Structure-from-Motion (SfM) aims to recover 3D scene structures and camera poses based on the correspondences between input images, and thus the ambiguity caused by duplicate structures (i.e., different structures with strong visual resemblance) always results in incorrect camera poses and 3D structures. To deal with the ambiguity, most existing studies resort to additional constraint information or implicit inference by analyzing two-view geometries or feature points. In this paper, we propose to exploit high-level information in the scene, i.e., the spatial contextual information of local regions, to guide the reconstruction. Specifically, a novel structure is proposed, namely, track-community, in which each community consists of a group of tracks and represents a local segment in the scene. A community detection algorithm is performed on the track-graph to partition the scene into segments. Then, the potential ambiguous segments are detected by analyzing the neighborhood of tracks and corrected by checking the pose consistency. Finally, we perform partial reconstruction on each segment and align them with a novel bidirectional consistency cost function which considers both 3D-3D correspondences and pairwise relative camera poses. Experimental results demonstrate that our approach can robustly alleviate reconstruction failure resulting from visually indistinguishable structures and accurately merge the partial reconstructions.
Gemini-LIGHTS: Herbig Ae/Be and Massive T Tauri Protoplanetary Disks Imaged with Gemini Planet Imager

We present the complete sample of protoplanetary disks from the Gemini- Large Imaging with the Gemini Planet Imager Herbig/T Tauri Survey, which observed bright Herbig Ae/Be stars and T Tauri stars in near-infrared polarized light to search for signatures of disk evolution and ongoing planet formation. The 44 targets were chosen based on their near- and mid-infrared colors, with roughly equal numbers of transitional, pre-transitional, and full disks. Our approach explicitly did not favor well-known, “famous” disks or those observed by the Atacama Large Millimeter/submillimeter Array, resulting in a less-biased sample suitable to probe the major stages of disk evolution during planet formation. Our optimized data reduction allowed polarized flux as low as 0.002% of the stellar light to be detected, and we report polarized scattered light around 80% of our targets. We detected point-like companions for 47% of the targets, including three brown dwarfs (two confirmed, one new), and a new super-Jupiter-mass candidate around V1295 Aql. We searched for correlations between the polarized flux and system parameters, finding a few clear trends: the presence of a companion drastically reduces the polarized flux levels, far-IR excess correlates with polarized flux for nonbinary systems, and systems hosting disks with ring structures have stellar masses <3 M⊙. Our sample also included four hot, dusty “FS CMa” systems, and we detected large-scale ( >100 au) scattered light around each, signs of extreme youth for these enigmatic systems. Science-ready images are publicly available through multiple distribution channels using a new FITS file standard that has been jointly developed with members of the Very Large Telescope Spectro-polarimetric High-contrast Exoplanet Research team.
Lower Bounds for Sorting 16, 17, and 18 Elements

It is a long-standing open question to determine the minimum number of comparisons $S(n)$ that suffice to sort an array of $n$ elements. Indeed, before this work $S(n)$ has been known only for $n\leq 22$ with the exception for $n=16$, $17$, and $18$. In this work, we fill that gap by proving that sorting $n=16$, $17$, and $18$ elements requires $46$, $50$, and $54$ comparisons respectively. This fully determines $S(n)$ for these values and disproves a conjecture by Knuth that $S(16) = 45$. Moreover, we show that for sorting $28$ elements at least 99 comparisons are needed. We obtain our result via an exhaustive computer search which extends previous work by Wells (1965) and Peczarski (2002, 2004, 2007, 2012). Our progress is both based on advances in hardware and on novel algorithmic ideas such as applying a bidirectional search to this problem.