Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Meta-Tuning for Content-aware Neural Video Delivery

Recently, Deep Neural Networks (DNNs) are utilized to reduce the bandwidth and improve the quality of Internet video delivery. Existing methods train corresponding content-aware super-resolution (SR) model for each video chunk on the server, and stream low-resolution (LR) video chunks along with SR models to the client. Although they achieve promising results, the huge computational cost of network training limits their practical applications. In this paper, we present a method named Efficient Meta-Tuning (EMT) to reduce the computational cost. Instead of training from scratch, EMT adapts a meta-learned model to the first chunk of the input video. As for the following chunks, it fine-tunes the partial parameters selected by gradient masking of previous adapted model. In order to achieve further speedup for EMT, we propose a novel sampling strategy to extract the most challenging patches from video frames. The proposed strategy is highly efficient and brings negligible additional cost. Our method significantly reduces the computational cost and achieves even better performance, paving the way for applying neural video delivery techniques to practical applications. We conduct extensive experiments based on various efficient SR architectures, including ESPCN, SRCNN, FSRCNN and EDSR-1, demonstrating the generalization ability of our work. The code is released at \url{https://github.com/Neural-video-delivery/EMT-Pytorch-ECCV2022}.
EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching

Recent advanced studies have spent considerable human efforts on optimizing network architectures for stereo matching but hardly achieved both high accuracy and fast inference speed. To ease the workload in network design, neural architecture search (NAS) has been applied with great success to various sparse prediction tasks, such as image classification and object detection. However, existing NAS studies on the dense prediction task, especially stereo matching, still cannot be efficiently and effectively deployed on devices of different computing capabilities. To this end, we propose to train an elastic and accurate network for stereo matching (EASNet) that supports various 3D architectural settings on devices with different computing capabilities. Given the deployment latency constraint on the target device, we can quickly extract a sub-network from the full EASNet without additional training while the accuracy of the sub-network can still be maintained. Extensive experiments show that our EASNet outperforms both state-of-the-art human-designed and NAS-based architectures on Scene Flow and MPI Sintel datasets in terms of model accuracy and inference speed. Particularly, deployed on an inference GPU, EASNet achieves a new SOTA 0.73 EPE on the Scene Flow dataset with 100 ms, which is 4.5$\times$ faster than LEAStereo with a better quality model.
ViGAT: Bottom-Up Event Recognition and Explanation in Video Using Factorized Graph Attention Network

In this paper a pure-attention bottom-up approach, called ViGAT, that utilizes an object detector together with a Vision Transformer (ViT) backbone network to derive object and frame features, and a head network to process these features for the task of event recognition and explanation in video, is proposed. The ViGAT head consists of graph attention network (GAT) blocks factorized along the spatial and temporal dimensions in order to capture effectively both local and long-term dependencies between objects or frames. Moreover, using the weighted in-degrees (WiDs) derived from the adjacency matrices at the various GAT blocks, we show that the proposed architecture can identify the most salient objects and frames that explain the decision of the network. A comprehensive evaluation study is performed, demonstrating that the proposed approach provides state-of-the-art results on three large, publicly available video datasets (FCVID, MiniKinetics, ActivityNet). Source code is made publicly available at: https://github.com/bmezaris/ViGAT
Influence of heavy resonances in SMASH

Recent lattice QCD results, comparing to a hadron resonance gas model, have shown the need for hundreds of particles in hadronic models. These extra particles influence both the equation of state and hadronic interactions within hadron transport models. Here, we introduce the PDG21+ particle list, which contains the most up-to-date database of particles and their properties. We then convert all particles decays into 2 body decays so that they are compatible with SMASH in order to produce a more consistent description of a heavy-ion collision.
The Details of Limb Brightening Reveal the Structure of the Base of the Jet in M87 for the First Time

It has become commonplace in astronomy to describe the transverse coarse structure of jets in loosely defined terms such as “sheath” and “spine” based on discussions of parsec scale properties. But, the applicability, dimension, and prominence of these features on sub-light-year scales has previously been unconstrained by observation. The first direct evidence of jet structure near the source in M87 is extreme limb brightening (a double-rail morphology), 0.3–0.6 mas from the source, which is prominent in observations with high resolution and sensitivity. Intensity crosscuts of these images provide three strong, interdependent constraints on the geometry responsible for the double-rail morphology: the rail to rail separation, the peak to trough intensity ratio, and the rail widths. Analyzing these constraints indicates that half or more of the jet volume resides in a thick-walled, tubular, mildly relativistic, protonic jet only ∼0.25 lt-yr (or ∼300 M, where M is the central black hole mass in geometrized units) from the source. By contrast, the Event Horizon Telescope Collaboration interprets their observations with the aid of general relativistic magnetohydrodynamic simulations that produce an invisible (by construction) jet with a surrounding luminous, thin sheath. Yet, it is shown that synthetic images of simulated jets are center brightened 0.3–0.6 mas from the source. This serious disconnection with observation occurs in a region previously claimed in the literature to be well represented by the simulations. The limb brightening analysis motivates a discussion of possible simulation modifications to improve conformance with observations.
Variance Reduction and Noise Source Sampling Techniques for Monte Carlo Simulations of Neutron Noise Induced by Mechanical Vibrations

Abstract Neutron noise in nuclear power reactors refers to the small fluctuations around the average neutron flux at steady state resulting from time-dependent perturbations inside the core. The neutron noise equations in the frequency domain can be solved using Monte Carlo simulation codes, which are capable of obtaining reference solutions involving almost no approximations but are hindered by severe issues affecting the statistical convergence: The simultaneous presence of positive and negative particles, which is required by the nature of the complex noise equations, leads to catastrophically large variance in the tallies. In this work, we consider the important case of neutron noise problems induced by mechanical vibrations. First, we derive a new exact sampling strategy for the noise source. Then, building upon our previous findings in other contexts, we show that weight cancellation methods can be highly beneficial in dealing with the presence of negative weights, enabling extremely large gains in the figure of merit. We successfully demonstrate our results on a benchmark configuration consisting of a fuel assembly with a vibrating pin, and we discuss possible pathways for further improvements.
Rethinking Few-Shot Class-Incremental Learning with Open-Set Hypothesis in Hyperbolic Geometry

Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding the overfitting and catastrophic forgetting simultaneously. The current protocol of FSCIL is built by mimicking the general class-incremental learning setting, while it is not totally appropriate due to the different data configuration, i.e., novel classes are all in the limited data regime. In this paper, we rethink the configuration of FSCIL with the open-set hypothesis by reserving the possibility in the first session for incoming categories. To assign better performances on both close-set and open-set recognition to the model, Hyperbolic Reciprocal Point Learning module (Hyper-RPL) is built on Reciprocal Point Learning (RPL) with hyperbolic neural networks. Besides, for learning novel categories from limited labeled data, we incorporate a hyperbolic metric learning (Hyper-Metric) module into the distillation-based framework to alleviate the overfitting issue and better handle the trade-off issue between the preservation of old knowledge and the acquisition of new knowledge. The comprehensive assessments of the proposed configuration and modules on three benchmark datasets are executed to validate the effectiveness concerning three evaluation indicators.
Is an Object-Centric Video Representation Beneficial for Transfer?

The objective of this work is to learn an object-centric video representation, with the aim of improving transferability to novel tasks, i.e., tasks different from the pre-training task of action classification. To this end, we introduce a new object-centric video recognition model based on a transformer architecture. The model learns a set of object-centric summary vectors for the video, and uses these vectors to fuse the visual and spatio-temporal trajectory 'modalities' of the video clip. We also introduce a novel trajectory contrast loss to further enhance objectness in these summary vectors. With experiments on four datasets -- SomethingSomething-V2, SomethingElse, Action Genome and EpicKitchens -- we show that the object-centric model outperforms prior video representations (both object-agnostic and object-aware), when: (1) classifying actions on unseen objects and unseen environments; (2) low-shot learning of novel classes; (3) linear probe to other downstream tasks; as well as (4) for standard action classification.
MANI-Rank: Multiple Attribute and Intersectional Group Fairness for Consensus Ranking

Combining the preferences of many rankers into one single consensus ranking is critical for consequential applications from hiring and admissions to lending. While group fairness has been extensively studied for classification, group fairness in rankings and in particular rank aggregation remains in its infancy. Recent work introduced the concept of fair rank aggregation for combining rankings but restricted to the case when candidates have a single binary protected attribute, i.e., they fall into two groups only. Yet it remains an open problem how to create a consensus ranking that represents the preferences of all rankers while ensuring fair treatment for candidates with multiple protected attributes such as gender, race, and nationality. In this work, we are the first to define and solve this open Multi-attribute Fair Consensus Ranking (MFCR) problem. As a foundation, we design novel group fairness criteria for rankings, called MANI-Rank, ensuring fair treatment of groups defined by individual protected attributes and their intersection. Leveraging the MANI-Rank criteria, we develop a series of algorithms that for the first time tackle the MFCR problem. Our experimental study with a rich variety of consensus scenarios demonstrates our MFCR methodology is the only approach to achieve both intersectional and protected attribute fairness while also representing the preferences expressed through many base rankings. Our real world case study on merit scholarships illustrates the effectiveness of our MFCR methods to mitigate bias across multiple protected attributes and their intersections.
AU-Supervised Convolutional Vision Transformers for Synthetic Facial Expression Recognition

The paper describes our proposed methodology for the six basic expression classification track of Affective Behavior Analysis in-the-wild (ABAW) Competition 2022. In Learing from Synthetic Data(LSD) task, facial expression recognition (FER) methods aim to learn the representation of expression from the artificially generated data and generalise to real data. Because of the ambiguous of the synthetic data and the objectivity of the facial Action Unit (AU), we resort to the AU information for performance boosting, and make contributions as follows. First, to adapt the model to synthetic scenarios, we use the knowledge from pre-trained large-scale face recognition data. Second, we propose a conceptually-new framework, termed as AU-Supervised Convolutional Vision Transformers (AU-CVT), which clearly improves the performance of FER by jointly training auxiliary datasets with AU or pseudo AU labels. Our AU-CVT achieved F1 score as $0.6863$, accuracy as $0.7433$ on the validation set. The source code of our work is publicly available online: https://github.com/msy1412/ABAW4
Tailoring Self-Supervision for Supervised Learning

Recently, it is shown that deploying a proper self-supervision is a prospective way to enhance the performance of supervised learning. Yet, the benefits of self-supervision are not fully exploited as previous pretext tasks are specialized for unsupervised representation learning. To this end, we begin by presenting three desirable properties for such auxiliary tasks to assist the supervised objective. First, the tasks need to guide the model to learn rich features. Second, the transformations involved in the self-supervision should not significantly alter the training distribution. Third, the tasks are preferred to be light and generic for high applicability to prior arts. Subsequently, to show how existing pretext tasks can fulfill these and be tailored for supervised learning, we propose a simple auxiliary self-supervision task, predicting localizable rotation ( LoRot ). Our exhaustive experiments validate the merits of LoRot as a pretext task tailored for supervised learning in terms of robustness and generalization capability. Our code is available at https://github.com/wjun0830/Localizable-Rotation.
Maximal X-ray feedback in the pre-reionization universe

X-ray feedback in the pre-reionization Universe provided one of the major energy sources for reionization and the thermal evolution of the early intergalactic medium. However, X-ray sources at high redshift have remained largely inaccessible to observations. One alternative approach to study the overall effects of X-ray feedback in the early Universe is a full cosmological simulation. Toward this goal, in this paper we create an analytic model of X-ray feedback from accretion on to supermassive black holes (SMBHs), to be used as a sub-grid model in future cosmological simulations. Our analytic model provides a relation between the mass of a dark matter halo and the SMBH it hosts, where the efficiency is governed by an energy balance argument between thermal feedback and the confining gravitational potential of the halo. To calibrate the model, we couple the halo-level recipe with the Press-Schechter halo mass function and derive global mass and energy densities. We then compare our model to various observational constraints, such as the resulting soft X-ray and IR cosmic radiation backgrounds, to test our choice of model parameters. We in particular derive model parameters that do not violate any constraints, while providing maximal X-ray feedback prior to reionization. In addition, we consider the contribution of SMBH X-ray sources to reionization and the global 21 cm absorption signal.
Locality Guidance for Improving Vision Transformers on Tiny Datasets

While the Vision Transformer (VT) architecture is becoming trendy in computer vision, pure VT models perform poorly on tiny datasets. To address this issue, this paper proposes the locality guidance for improving the performance of VTs on tiny datasets. We first analyze that the local information, which is of great importance for understanding images, is hard to be learned with limited data due to the high flexibility and intrinsic globality of the self-attention mechanism in VTs. To facilitate local information, we realize the locality guidance for VTs by imitating the features of an already trained convolutional neural network (CNN), inspired by the built-in local-to-global hierarchy of CNN. Under our dual-task learning paradigm, the locality guidance provided by a lightweight CNN trained on low-resolution images is adequate to accelerate the convergence and improve the performance of VTs to a large extent. Therefore, our locality guidance approach is very simple and efficient, and can serve as a basic performance enhancement method for VTs on tiny datasets. Extensive experiments demonstrate that our method can significantly improve VTs when training from scratch on tiny datasets and is compatible with different kinds of VTs and datasets. For example, our proposed method can boost the performance of various VTs on tiny datasets (e.g., 13.07% for DeiT, 8.98% for T2T and 7.85% for PVT), and enhance even stronger baseline PVTv2 by 1.86% to 79.30%, showing the potential of VTs on tiny datasets. The code is available at https://github.com/lkhl/tiny-transformers.
Weakly Supervised Visual-Auditory Human-eye Fixation Prediction with Multigranularity Perception

—Thanks to the rapid advances in deep learning techniques and the wide availability of large-scale training sets, the performance of video saliency detection models has been improving steadily and signiﬁcantly. However, deep learning-based visual-audio ﬁxation prediction is still in its infancy. At present, only a few visual-audio sequences have been furnished, with real ﬁxations being recorded in real visual-audio environments. Hence, it would be neither efﬁcient nor necessary to recollect real ﬁxations under the same visual-audio circumstances. To address this problem, this paper promotes a novel approach in a weakly supervised manner to alleviate the demand of large-scale training sets for visual-audio model training. By using only the video category tags, we propose the s elective c lass a ctivation m apping (SCAM) and its upgrade (SCAM+). In the spatial-temporal-audio circumstance, the former follows a coarse-to-ﬁne strategy to select the most discriminative regions, and these regions are usually capable of exhibiting high consistency with the real human-eye ﬁxations. The latter equips the SCAM with an additional multi-granularity perception mechanism, making the whole process more consistent with that of the real human visual system. Moreover, we distill knowledge from these regions to obtain complete new s patial-t emporal-a udio (STA) f ixation p rediction (FP) networks, enabling broad applications in cases where video tags are not available. Without resorting to any real human-eye ﬁxation, the performances of these STA FP networks are comparable to those of fully supervised networks. The code and results are publicly available at https://github.com/guotaowang/STANet.
A kind of control technology for squeezing failure in deep roadways: a case study

ABSTRACT Under high horizontal ground stress, the squeezing failure has been a common failure pattern for deep roadway. And the large deformation of surrounding rock mass around the roadway has also been a major challenge for deep mining practices. This paper describes a case study of the failure mechanisms and stability control technology of deep roadway under high horizontal ground stress in Jinchuan mine in Gansu Province, China. And this paper also aims to propose a kind of support strategy to prevent the plastic slippage around the floor of roadway. This study started with an in situ broken zone measurement programme to determine the depth of broken zone at each part of the roadway. Combined with long bolt and floor beam, an improved support strategy has been proposed. The field test results show that the improved support strategy can successfully solve the problems of side wall collapse and floor heaven. It can significantly restricts rock mass large deformation and avoid frequent repair. Compare to the original support, the improved support strategy can greatly save investment of mines and also has good application value.
Independences and partial $R$-transforms in bi-free probability

In this paper, we examine how various notions of independence in non-commutative probability theory arise in bi-free probability. We exhibit how Boolean and monotone independence occur from bi-free pairs of faces and establish a Kac/Loeve Theorem for bi-free independence. In addition, we prove that bi-freeness is preserved under tensoring with matrices. Finally, via combinatorial arguments, we construct partial $R$-transforms in two settings relating the moments and cumulants of a left-right pair of operators.
Search for periodicities in the light variations of the Seyfert galaxy NGC 4151

The optical luminosity variations of the nucleus of the Seyfert galaxy NGC 4151 are examined. Power spectrum analysis is performed on heretofore unpublished photographic photometry as well as on the published photographic and photoelectric photometric data of many observers. Contrary to previous suggestions, no evidence for any significant periodicities on timescales ranging from days to decades was found.
Expectation-Propagation for Summary-Less, Likelihood-Free Inference

Many models of interest in the natural and social sciences have no closed-form likelihood function, which means that they cannot be treated using the usual techniques of statistical inference. In the case where such models can be efficiently simulated, Bayesian inference is still possible thanks to the Approximate Bayesian Computation (ABC) algorithm. Although many refinements have since been suggested, the technique suffers from three major shortcomings. First, it requires introducing a vector of “summary statistics”, the choice of which is arbitrary and may lead to strong biases. Second, ABC may be excruciatingly slow due to very low acceptance rates. Third, it cannot produce a reliable estimate of the marginal likelihood of the model. We introduce a technique that solves the first and the third issues, and considerably alleviates the second. We adapt to the likelihood-free context a variational approximation algorithm, Expectation Propagation (Minka, 2001). The resulting algorithm is shown to be faster by a few orders of magnitude than alternative algorithms, while producing an overall approximation error which is typically negligible. Comparisons are performed in three real-world applications which are typical of likelihood-free inference, including one application in neuroscience which is novel, and possibly too challenging for standard ABC techniques. Key-words: Approximate Bayesian Computation; Expectation Propagation; Likelihood-Free Inference; Quasi-Monte Carlo.
Design and Performance Investigation of the Double-Sided Linear Induction Motor With a Ladder-Slot Secondary

This paper proposes an analytical model to design and investigates the double-sided linear induction machine with a ladder-slot arrangement employed on the secondary mover. Emphasizing the longitudinal end effect, the equivalent circuits are based on Duncan's model modified by electromagnetic field analysis. By defining the ratio of ladder width to slot width (the ladder–slot ratio) equivalent circuit parameters, including the magnetizing inductance and resistance, and the secondary resistance, are estimated using formulae linked to the dimensions and geometry of the primary and secondary. The analytical model is proven to be effective through measurements made on an experimental machine. Magnetizing inductance and resistance estimates are shown to be predicted accurately using the analytical method, and the analytical method is further simplified for rapid and efficient practical design. The major contribution of this research is a practical method to optimize the design of large-scale drives using linear induction motors with a ladder–slot arrangement.