Miracle-dz/UniFAR-FTUs huggingface dataset:
Inductive Attention for Video Action Anticipation

Anticipating future actions based on spatiotemporal observations is essential in video understanding and predictive computer vision. Moreover, a model capable of anticipating the future has important applications, it can benefit precautionary systems to react before an event occurs. However, unlike in the action recognition task, future information is inaccessible at observation time -- a model cannot directly map the video frames to the target action to solve the anticipation task. Instead, the temporal inference is required to associate the relevant evidence with possible future actions. Consequently, existing solutions based on the action recognition models are only suboptimal. Recently, researchers proposed extending the observation window to capture longer pre-action profiles from past moments and leveraging attention to retrieve the subtle evidence to improve the anticipation predictions. However, existing attention designs typically use frame inputs as the query which is suboptimal, as a video frame only weakly connects to the future action. To this end, we propose an inductive attention model, dubbed IAM, which leverages the current prediction priors as the query to infer future action and can efficiently process the long video content. Furthermore, our method considers the uncertainty of the future via the many-to-many association in the attention design. As a result, IAM consistently outperforms the state-of-the-art anticipation models on multiple large-scale egocentric video datasets while using significantly fewer model parameters.
The sub-bureau for atmospheric angular momentum of the International Earth Rotation Service: A meteorological data center with geodetic applications

By exchanging angular momentum with the solid portion of the earth, the atmosphere plays a vital role in exciting small but measurable changes in the rotation of our planet. Recognizing this relationship, the International Earth Rotation Service invited the U.S. National Meteorological Center to organize a Sub-bureau for Atmospheric Angular Momentum (SBAAM) for the purpose of collecting, distributing, archiving, and analyzing atmospheric parameters relevant to earth rotation/polar motion. These functions of wind and surface pressure are being computed with data from several of the world's weather services, and they are being widely applied to the research and operations of the geodetic community. The SBAAM began operating formally in October 1989, and this article highlights its development, operations, and significance.
3D Point Cloud Pre-training with Knowledge Distillation from 2D Images

The recent success of pre-trained 2D vision models is mostly attributable to learning from large-scale datasets. However, compared with 2D image datasets, the current pre-training data of 3D point cloud is limited. To overcome this limitation, we propose a knowledge distillation method for 3D point cloud pre-trained models to acquire knowledge directly from the 2D representation learning model, particularly the image encoder of CLIP, through concept alignment. Specifically, we introduce a cross-attention mechanism to extract concept features from 3D point cloud and compare them with the semantic information from 2D images. In this scheme, the point cloud pre-trained models learn directly from rich information contained in 2D teacher models. Extensive experiments demonstrate that the proposed knowledge distillation scheme achieves higher accuracy than the state-of-the-art 3D pre-training methods for synthetic and real-world datasets on downstream tasks, including object classification, object detection, semantic segmentation, and part segmentation.
Riemannian Optimization for Variance Estimation in Linear Mixed Models

Variance parameter estimation in linear mixed models is a challenge for many classical nonlinear optimization algorithms due to the positive-definiteness constraint of the random effects covariance matrix. We take a completely novel view on parameter estimation in linear mixed models by exploiting the intrinsic geometry of the parameter space. We formulate the problem of residual maximum likelihood estimation as an optimization problem on a Riemannian manifold. Based on the introduced formulation, we give geometric higher-order information on the problem via the Riemannian gradient and the Riemannian Hessian. Based on that, we test our approach with Riemannian optimization algorithms numerically. Our approach yields a higher quality of the variance parameter estimates compared to existing approaches.
Designing Culturally Aware Learning Analytics: A Value Sensitive Perspective

This chapter aims to stress the importance of addressing culture when designing and implementing learning analytics services. Learning analytics have been implemented in different countries with the purpose of improving learning and supporting teaching; yet, largely at a limited scale and so far with limited evidence of achieving their purpose. Even though some solutions seem promising, their transfer from one country to another might prove challenging and sometimes impossible due to various technical, social, contextual and cultural factors. In this chapter, we argue for a need to carefully consider one of these – largely underexplored by the learning analytics community – factors, namely cultural values when designing and implementing learning analytics systems. Viewing culture from a value-sensitive perspective, in this chapter, we: 1. exemplify two selected values (i.e., privacy and autonomy) that might play a significant role in the design of learning analytics systems, and 2. discuss opportunities for applying culture- and value-sensitive design methods that can guide the design of culturally aware learning analytics systems. Finally, a set of design implications for culturally aware and value-sensitive learning analytics services is offered.
Full counting statistics as probe of measurement-induced transitions in the quantum Ising chain

Non-equilibrium dynamics of many-body quantum systems under the effect of measurement protocols is attracting an increasing amount of attention. It has been recently revealed that measurements may induce different non-equilibrium regimes and an abrupt change in the scaling-law of the bipartite entanglement entropy. However, our understanding of how these regimes appear{, how they affect the statistics of local quantities and}, finally whether they survive in the thermodynamic limit, is much less established. Here we investigate measurement-induced phase transitions in the Quantum Ising chain coupled to a monitoring environment. In particular we show that local projective measurements induce a quantitative modification of the out-of-equilibrium probability distribution function of the local magnetization. Starting from a GHZ state, the relaxation of the paramagnetic and the ferromagnetic order is analysed. In particular we describe how the probability distributions associated to them show different behaviour depending on the measurement rate.
Dual action of light in photodarkened Ge–As–S films

In annealed chalcogenide films, thinner than 1 µm, highly expressed photodarkening (PD) can be fully restored after prolonged illumination. It was supposed that this light annealing effect (LAE) results from the dual action of light, which provokes not only increase of network disorder at PD but also decrease of disorder leading to photobleaching (PB), competing with PD. To confirm it, the changes of two parameters of disorder are studied: the Tauc slope B1/2, dependent on network bonding character, and the Urbach energy Eu, associated with tailing of the band edges. It is found that B1/2 decreases during PD and increases when PB overcomes the PD, which is direct evidence that the basis of the LAE is the dual action of light. The changes of disorder defined by Eu are found to be mostly inversely related to the changes of B1/2. The peculiarities of this relationship after various treatments are presented and the problem of the correspondence of both parameters of disorder is discussed.
Photo-Realistic Out-of-domain GAN inversion via Invertibility Decomposition

The ﬁdelity of Generative Adversarial Networks (GAN) inversion is impeded by Out-Of-Domain (OOD) areas (e.g., background, accessories) in the image. Detecting the OOD areas beyond the generation ability of the pretrained model and blending these regions with the input image can enhance ﬁdelity. The “invertibility mask” ﬁgures out these OOD areas, and existing methods predict the mask with the reconstruction error. However, the estimated mask is usually inaccurate due to the inﬂuence of the reconstruction error in the In-Domain (ID) area. In this paper, we propose a novel framework that enhances the ﬁdelity of human face inversion by designing a new module to decompose the input images to ID and OOD partitions with invertibility masks. Unlike previous works, our invertibility detector is simultaneously learned with a spatial alignment module. We iteratively align the generated features to the input geometry and reduce the reconstruction error in the ID regions. Thus, the OOD areas are more distinguishable and can be precisely predicted. Then, we improve the ﬁdelity of our results by blending the OOD areas from the input image with the ID GAN inversion results. Our method produces photo-realistic results for real-world human face image inversion and manipulation. Extensive experiments demonstrate our method’s superiority over existing methods in the quality of GAN inversion and attribute manipulation. Our code is available at: https://github.com/AbnerVictor/OOD-GAN-inversion
Open-loop and closed-loop local and remote stochastic nonzero-sum game with inconsistent information structure

In this paper, the open-loop and closed-loop local and remote stochastic nonzero-sum game (LRSNG) problem is investigated. Different from previous works, the stochastic nonzero-sum game problem under consideration is essentially a special class of two-person nonzero-sum game problem, in which the information sets accessed by the two players are inconsistent. More specifically, both the local player and the remote player are involved in the system dynamics, and the information sets obtained by the two players are different, and each player is designed to minimize its own cost function. For the considered LRSNG problem, both the open-loop and closed-loop Nash equilibrium are derived. The contributions of this paper are given as follows. Firstly, the open-loop optimal Nash equilibrium is derived, which is determined in terms of the solution to the forward and backward stochastic difference equations (FBSDEs). Furthermore, by using the orthogonal decomposition method and the completing square method, the feedback representation of the optimal Nash equilibrium is derived for the first time. Finally, the effectiveness of our results is verified by a numerical example.
Robust Predictive Output-Feedback Safety Filter for Uncertain Nonlinear Control Systems

In real-world applications, we often require reliable decision making under dynamics uncertainties using noisy high-dimensional sensory data. Recently, we have seen an increasing number of learning-based control algorithms developed to address the challenge of decision making under dynamics uncertainties. These algorithms often make assumptions about the underlying unknown dynamics and, as a result, can provide safety guarantees. This is more challenging for other widely used learning-based decision making algorithms such as reinforcement learning. Furthermore, the majority of existing approaches assume access to state measurements, which can be restrictive in practice. In this paper, inspired by the literature on safety filters and robust output-feedback control, we present a robust predictive output-feedback safety filter (RPOF-SF) framework that provides safety certification to an arbitrary controller applied to an uncertain nonlinear control system. The proposed RPOF-SF combines a robustly stable observer that estimates the system state from noisy measurement data and a predictive safety filter that renders an arbitrary controller safe by (possibly) minimally modifying the controller input to guarantee safety. We show in theory that the proposed RPOF-SF guarantees constraint satisfaction despite disturbances applied to the system. We demonstrate the efficacy of the proposed RPOF-SF algorithm using an uncertain mass-spring-damper system.
Constraints on Stellar Flare Energy Ratios in the NUV and Optical from a Multiwavelength Study of GALEX and Kepler Flare Stars

We present a multiwavelength study of stellar flares on primarily G-type stars using overlapping time domain surveys in the near-ultraviolet (NUV) and optical regimes. The NUV (the Galaxy Evolution Explorer; GALEX) and optical (Kepler) wavelength domains are important for understanding energy fractionations in stellar flares, and for constraining the associated incident radiation on a planetary atmosphere. We follow up on the NUV flare detections presented in Brasseur et al., using coincident Kepler long (1557 flares) and short (two flares) cadence light curves. We find no evidence of optical flares at these times, and place limits on the flare energy ratio between the two wave bands. We find that the energy ratio is correlated with GALEX band energy, and extends over a range of about 3 orders of magnitude in the ratio of the upper limit of Kepler band flare energy to NUV flare energy at the same time for each flare. The two flares with Kepler short-cadence data indicate that the true Kepler band energy may be much lower than the long-cadence-based upper limit. A similar trend appears for the bulk flare energy properties of nonsimultaneously observed flares on the same stars. We provide updated models to describe the flare spectral energy distribution from the NUV through the optical including continua and emission lines to improve upon blackbody-only models. The spread of observed energy ratios is much larger than encompassed by these models and suggests new physics is at work. These results call for a better understanding of NUV flare physics and provide a cautionary tale about using only optical flare measurements to infer the UV irradiation of close-in planets.
'Rarely' a problem? Language models exhibit inverse scaling in their predictions following 'few'-type quantifiers

How well do language models deal with quantification? In this study, we focus on 'few'-type quantifiers, as in 'few children like toys', which might pose a particular challenge for language models because the sentence components with out the quantifier are likely to co-occur, and 'few'-type quantifiers are rare. We present 960 English sentence stimuli from two human neurolinguistic experiments to 22 autoregressive transformer models of differing sizes. Not only do all the models perform poorly on 'few'-type quantifiers, but overall the larger the model, the worse its performance. This inverse scaling is consistent with previous work suggesting that larger models increasingly reflect online rather than offline human processing, and we argue that the decreasing performance of larger models may challenge uses of language models as the basis for natural language systems.
Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models

World models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios such as autonomous driving, there commonly exists noncontrollable dynamics independent of the action signals, making it difficult to learn effective world models. To tackle this problem, we present a novel reinforcement learning approach named Iso-Dream, which improves the Dream-to-Control framework in two aspects. First, by optimizing the inverse dynamics, we encourage the world model to learn controllable and noncontrollable sources of spatiotemporal changes on isolated state transition branches. Second, we optimize the behavior of the agent on the decoupled latent imaginations of the world model. Specifically, to estimate state values, we roll-out the noncontrollable states into the future and associate them with the current controllable state. In this way, the isolation of dynamics sources can greatly benefit long-horizon decision-making of the agent, such as a self-driving car that can avoid potential risks by anticipating the movement of other vehicles. Experiments show that Iso-Dream is effective in decoupling the mixed dynamics and remarkably outperforms existing approaches in a wide range of visual control and prediction domains.
Supercooled liquids are Fickian yet non-Gaussian

Reply to"Comment on 'Fickian non-Gaussian diffusion in glass-forming liquids'". In [ArXiv:2210.07119v1], Berthier et al. questioned the findings of our letter [Phys. Rev. Lett. 128, 168001 (2022)], concerning the existence and the features of Fickian non-Gaussian diffusion in glass-forming liquids. Here we demonstrate that their arguments are either wrong, or not meaningful to our scope. Thus, we fully confirm the validity and novelty of our results.
Shock waves for a model system of the radiating gas

This paper is concerned with the existence and the asymptotic stability of traveling waves for a model system derived from approximating the one-dimensional system of the radiating gas. We show the existence of smooth or discontinuous traveling waves and also prove the uniqueness of these traveling waves under the entropy condition, in the class of piecewise smooth functions with the first kind discontinuities. Furthermore, we show that the C3 -smooth traveling waves areasymptotically stable and that the rate of convergence toward these waves is t-1/4 , which looks optimal. The proof of stability is given by applying the standard energy method to the integrated equation of the original one.
GAN-based Tabular Data Generator for Constructing Synopsis in Approximate Query Processing: Challenges and Solutions

In data-driven systems, data exploration is imperative for making real-time decisions. However, big data are stored in massive databases that are difficult to retrieve. Approximate Query Processing (AQP) is a technique for providing approximate answers to aggregate queries based on a summary of the data (synopsis) that closely replicates the behavior of the actual data; this can be useful when an approximate answer to queries is acceptable in a fraction of the real execution time. This study explores the novel utilization of a Generative Adversarial Network (GAN) for the generation of tabular data that can be employed in AQP for synopsis construction. We thoroughly investigate the unique challenges posed by the synopsis construction process, including maintaining data distribution characteristics, handling bounded continuous and categorical data, and preserving semantic relationships, and we then introduce the advancement of tabular GAN architectures that overcome these challenges. Furthermore, we propose and validate a suite of statistical metrics tailored for assessing the reliability of GAN-generated synopses. Our findings demonstrate that advanced GAN variations exhibit a promising capacity to generate high-fidelity synopses, potentially transforming the efficiency and effectiveness of AQP in data-driven systems.
Combining inconsistent data from interlaboratory comparisons

The most widely used procedure for finding the value of a quantity from data obtained from interlaboratory comparisons involves calculating the inverse-variance weighted mean of the laboratories' estimates. This method produces adequate results if the data are consistent. However, sometimes a consistency test reveals the possible existence of outliers that nevertheless have to be included in the evaluation task. In this paper the Bayesian understanding of probability is used to derive three methods that may be applied in the case when the data appear to be inconsistent. The performances of these methods are compared through a simulated example.
Factors Required for Adhesion of Salmonella enterica Serovar Typhimurium to Lactuca sativa (Lettuce)

The number of produce-associated outbreaks by gastrointestinal pathogens is increasing and underlines the relevance to human health. The mechanisms involved in the colonization of, persistence on, and transmission by, fresh produce are poorly understood. ABSTRACT Salmonella enterica serovar Typhimurium is a major cause of foodborne gastroenteritis. Recent outbreaks of infections by S. enterica serovar Typhimurium are often associated with non-animal-related food, i.e., vegetables, fruits, herbs, sprouts, and nuts. One main problem related to the consumption of fresh produce is the minimal processing, especially for leafy green salads. In this study, we focused on butterhead lettuce (Lactuca sativa) to which S. enterica serovar Typhimurium adheres at higher rates compared to Valerianella locusta, resulting in prolonged persistence. Here, we systematically analyzed factors contributing to adhesion of S. enterica serovar Typhimurium to L. sativa leaves. Application of a reductionist, synthetic approach, including the controlled surface expression of specific adhesive structures of S. enterica serovar Typhimurium, one at a time, enabled the identification of relevant fimbrial and nonfimbrial adhesins, the O-antigen of lipopolysaccharide, the flagella, and chemotaxis being involved in binding to L. sativa leaves. The analyses revealed contributions of Lpf fimbriae, Sti fimbriae, autotransported adhesin MisL, T1SS-secreted BapA, intact lipopolysaccharide (LPS), and flagella-mediated motility to adhesion of S. enterica serovar Typhimurium to L. sativa leaves. In addition, we identified BapA as a potential adhesin involved in binding to V. locusta and L. sativa leaf surfaces. IMPORTANCE The number of produce-associated outbreaks by gastrointestinal pathogens is increasing and underlines the relevance to human health. The mechanisms involved in the colonization of, persistence on, and transmission by, fresh produce are poorly understood. Here, we investigated the contribution of adhesive factors of S. enterica serovar Typhimurium in the initial phase of plant colonization, i.e., the binding to the plant surface. We used the previously established reductionist, synthetic approach to identify factors that contribute to the surface binding of S. enterica serovar Typhimurium to leaves of L. sativa by expressing all known adhesive structures by remote control expression system.
Deep Learning for Reliable Communication Optimization on Autonomous Vehicles

Recent breakthroughs in the autonomous vehicle industry have brought this technology closer to consumers. However, the cost of self-driving solutions still constitutes an entry barrier to many potential users due to its reliance on powerful onboard computers. As an alternative, autonomous driving algorithm processing may be offloaded to remote machines, which requires a reliable connection to the cloud servers. However, despite significant 5G coverage in many countries, mobile network reliability and latency are still inadequate for this purpose. This work explores deep learning concepts to forecast signal quality as a vehicle moves, predicting when periods of degraded network quality will occur. We develop a Long Short-Term Memory (LSTM)-based neural network, trained on multivariate time series containing historical data on several mobile network parameters, and evaluate the results of multi-step Reference Signal Received Power (RSRP) prediction. Results show that our model achieves a rapidly increasing Root-Mean-Square Error (RMSE), reaching over 8 dBm after 25-time steps. This error does not allow for the accurate prediction of future signal quality.