Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of social support on posttraumatic stress symptoms among children and adolescents: a scoping review and meta-analysis

ABSTRACT Background Several risk and protective factors play a role in the development of posttraumatic stress symptoms (PTSS) in children and youth. The evidence for social support (SS) as a protective factor is rising; however, a review of the evidence is lacking. Objective This scoping review and meta-analysis aims to map out and synthesize the present research on the influence of social support on PTSS in children and adolescents. Method The literature searched through PubMed, MEDLINE, Embase, PsycInfo, and CINAHL identified 3629 abstracts. Ninety articles published from 1999 to 2020 were selected, including a total of 77,439 participants. Results Most papers focused on social and emotional support from family members (36/88) and peers (26/88); other types of support (e.g., informational support and support from professionals) were not widely reported. The cross-sectional studies illustrated an overall significant, but weak, negative correlation between global social support and PTSS. A similar weak negative association was found between family support and PTSS. The association between peer support and PTSS was not significant. For longitudinal studies, 4 of 5 indicated that SS was a significant negative predictor of PTSS. Conclusions There was conceptual, methodological, and statistical heterogeneity of the identified studies. This review suggests a weak negative relationship between global SS and PTSS in children and adolescents. Higher global SS was related to less PTSS. The evidence regarding family support revealed a more stable negative relationship with PTSS than that for peer support. Investigating social support without specifying the form of support confounds the effect. Studies on informational, teacher, or professional support seem to be lacking. More studies are needed on the longitudinal effects of SS on PTSS. HIGHLIGHTS This study aims to review current knowledge on the influence of SS on children and adolescents with traumatic experiences and/or posttraumatic stress symptoms (PTSS). A systematic search strategy identified 3629 abstracts, among which 90 quantitative articles were selected. Our review found that there was a weak negative relationship between global SS and PTSS in children and adolescents and that family support showed a more robust protective effect than peer support.
Facial Expression Classification using Fusion of Deep Neural Network in Video

For computers to recognize human emotions, expression classification is an equally important problem in the human-computer interaction area. In the 3rd Affective Behavior Analysis In-The-Wild competition, the task of expression classification includes eight classes with six basic expressions of human faces from videos. In this paper, we employ a transformer mechanism to encode the robust representation from the backbone. Fusion of the robust representations plays an important role in the expression classification task. Our approach achieves 30.35% and 28.60% for the F1 score on the validation set and the test set, respectively. This result shows the effectiveness of the proposed architecture based on the Aff-Wild2 dataset and our team archives 5th for the expression classification task in the 3rd Affective Behavior Analysis In-The-Wild competition.
Angstrofluidics: Walking to the Limit

Angstrom-scale fluidic channels are ubiquitous in nature and play an important role in regulating cellular traffic, signaling, and responding to stimuli. Synthetic angstrom channels are now a reality with the emergence of several cutting-edge bottom-up and top-down fabrication methods. In particular, the use of atomically thin 2D materials and nanotubes as components to build fluidic conduits has pushed the limits of fabrication to the angstrom scale. Here, we provide an overview of recent developments in the fabrication methods for nano- and angstrofluidic channels while categorizing them on the basis of dimensionality (0D pores, 1D tubes, 2D slits), along with the latest advances in measurement techniques. We discuss the ion transport governed by various stimuli in these channels and the variation of ionic mobility, streaming power, and osmotic power with pore size across all the dimensionalities. Finally, we highlight unique future opportunities in the development of smart ionic devices.
Learning Disentangled Representation for One-shot Progressive Face Swapping

Although face swapping has attracted much attention in recent years, it remains a challenging problem. The existing methods leverage a large number of data samples to explore the intrinsic properties of face swapping without taking into account the semantic information of face images. Moreover, the representation of the identity information tends to be fixed, leading to suboptimal face swapping. In this paper, we present a simple yet efficient method named FaceSwapper, for one-shot face swapping based on Generative Adversarial Networks. Our method consists of a disentangled representation module and a semantic-guided fusion module. The disentangled representation module is composed of an attribute encoder and an identity encoder, which aims to achieve the disentanglement of the identity and the attribute information. The identity encoder is more flexible and the attribute encoder contains more details of the attributes than its competitors. Benefiting from the disentangled representation, FaceSwapper can swap face images progressively. In addition, semantic information is introduced into the semantic-guided fusion module to control the swapped area and model the pose and expression more accurately. The experimental results show that our method achieves state-of-the-art results on benchmark datasets with fewer training samples. Our code is publicly available at https://github.com/liqi-casia/FaceSwapper.
Vector-valued statistics of binomial processes: Berry–Esseen bounds in the convex distance

We study the discrepancy between the distribution of a vector-valued functional of i.i. d. random elements and that of a Gaussian vector. Our main contribution is an explicit bound on the convex distance between the two distributions, holding in every dimension. Such a finding constitutes a substantial extension of the one-dimensional bounds deduced in Chatterjee (2007) and Lachi\`eze-Rey and Peccati (2017), as well as of the multidimensional bounds for smooth test functions and indicators of rectangles derived, respectively, in Dung (2019), and Fang and Koike (2021). Our techniques involve the use of Stein's method, combined with a suitable adaptation of the recursive approach inaugurated by Schulte and Yukich (2017): this yields rates of converge that have a presumably optimal dependence on the sample size. We develop several applications of a geometric nature, among which is a new collection of multidimensional quantitative limit theorems for the intrinsic volumes associated with coverage processes in Euclidean spaces.
AIMusicGuru: Music Assisted Human Pose Correction

Pose Estimation techniques rely on visual cues available through observations represented in the form of pix-els. But the performance is bounded by the frame rate of the video and struggles from motion blur, occlusions, and temporal coherence. This issue is magniﬁed when people are interacting with objects and instruments, for example playing the violin. Standard approaches for postprocessing use interpolation and smoothing func-tions to ﬁlter noise and ﬁll gaps, but they cannot model highly non-linear motion. We present a method that leverages our understanding of the high degree of a causal relationship between the sound produced and the motion that produces them. We use the audio signa-ture to reﬁne and predict accurate human body pose motion models. We propose MAPnet (Music Assisted Pose network) for generating a ﬁne grain motion model from sparse input pose sequences but continuous audio. To accelerate further research in this domain, we also open-source MAPdat, a new multi-modal dataset of 3D violin playing motion with music. We perform a comparison of different standard machine learning models and perform analysis on input modalities, sampling techniques, and audio and motion features. Experiments on MAPdat suggest multi-modal approaches like ours as a promising direction for tasks previously approached with visual methods only. Our results show both quali-tatively and quantitatively how audio can be combined with visual observation to help improve any pose estimation methods.
Predicting the location of shear band initiation in a metallic glass

We report atomistic simulation results which indicate that the location of shear banding in a metallic glass (MG) can be ascertained with reasonably high accuracy solely from the undeformed static structure. Correlation is observed between the location of the initiation of shear bands in a simulated MG and the initial distribution of the density of fertile sites (DFS) for stress-driven shear transformations identified a priori based on a deep learning model devised in our recent work [Fan and Ma, Nat. Commun. 12 , 1506 (2021)]. In addition, we demonstrated that one can judge whether a glass is brittle or ductile solely based upon its initial DFS distribution. These validate that shear bands in MG arise from non-linear instabilities, and that the as-quenched glass structure contains inhomogeneities that influence these instabilities. This work also reveals an important subtlety regarding the non-deterministic nature of athermal quasistatic shear simulations.
ViT-FOD: A Vision Transformer based Fine-grained Object Discriminator

Recently, several Vision Transformer (ViT) based methods have been proposed for Fine-Grained Visual Classification (FGVC). These methods significantly surpass existing CNN-based ones, demonstrating the effectiveness of ViT in FGVC tasks.However, there are some limitations when applying ViT directly to FGVC.First, ViT needs to split images into patches and calculate the attention of every pair, which may result in heavy redundant calculation and unsatisfying performance when handling fine-grained images with complex background and small objects.Second, a standard ViT only utilizes the class token in the final layer for classification, which is not enough to extract comprehensive fine-grained information. To address these issues, we propose a novel ViT based fine-grained object discriminator for FGVC tasks, ViT-FOD for short. Specifically, besides a ViT backbone, it further introduces three novel components, i.e, Attention Patch Combination (APC), Critical Regions Filter (CRF), and Complementary Tokens Integration (CTI). Thereinto, APC pieces informative patches from two images to generate a new image so that the redundant calculation can be reduced. CRF emphasizes tokens corresponding to discriminative regions to generate a new class token for subtle feature learning. To extract comprehensive information, CTI integrates complementary information captured by class tokens in different ViT layers. We conduct comprehensive experiments on widely used datasets and the results demonstrate that ViT-FOD is able to achieve state-of-the-art performance.
Multi Expression Programming for solving classification problems

Multi Expression Programming (MEP) is a Genetic Programming variant which encodes multiple solutions in a single chromosome. This paper introduces and deeply describes several strategies for solving binary and multi-class classiﬁcation problems within the multi solutions per chromosome paradigm of MEP. Extensive experiments on various classiﬁcation problems are performed. MEP shows similar or better per-formances than other methods used for comparison (namely Artiﬁcial Neural Networks and Linear Genetic Programming).
Weakly-Supervised End-to-End CAD Retrieval to Scan Objects

CAD model retrieval to real-world scene observations has shown strong promise as a basis for 3D perception of objects and a clean, lightweight mesh-based scene representation; however, current approaches to retrieve CAD models to a query scan rely on expensive manual annotations of 1:1 associations of CAD-scan objects, which typically contain strong lower-level geometric differences. We thus propose a new weakly-supervised approach to retrieve semantically and structurally similar CAD models to a query 3D scanned scene without requiring any CAD-scan associations, and only object detection information as oriented bounding boxes. Our approach leverages a fully-differentiable top-$k$ retrieval layer, enabling end-to-end training guided by geometric and perceptual similarity of the top retrieved CAD models to the scan queries. We demonstrate that our weakly-supervised approach can outperform fully-supervised retrieval methods on challenging real-world ScanNet scans, and maintain robustness for unseen class categories, achieving significantly improved performance over fully-supervised state of the art in zero-shot CAD retrieval.
SMARAGD: Synthesized sMatch for Accurate and Rapid AMR Graph Distance

The semantic similarity of graph-based meaning representations, such as Abstract Meaning Representation (AMR), is typically assessed using graph matching algorithms, such as S MATCH (Cai and Knight, 2013). However, S MATCH suffers from NP-completeness, making its large-scale application, e.g., for AMR clustering or semantic search, infeasible. To mitigate this issue, we propose SMARAGD (Synthesized sMatch for accurate and rapid AMR graph distance). We show the potential of neural networks to approximate the S MATCH scores and graph alignments, i) in linear time using a machine translation framework to predict the alignments, or ii) in constant time using a Siamese CNN to directly predict S MATCH scores. We show that the approximation error can be substantially reduced by applying data augmentation and AMR graph anonymization.
Inverse reduction inequalities for spectral numbers and applications

A BSTRACT . Our main result is the proof of an inequality between the spectral numbers of a Lagrangian and the spectral numbers of its reductions, going in the opposite direction to the classical inequality (see e.g [Vit92]). This has applications to the "Geometrically bounded Lagrangians are spectrally bounded" conjecture from [Vit08] and to the structure of elements in the γ -completion of the set of exact Lagrangians (see [Vit22]). We also investigate the local path-connectedness of the set of Hamiltonian diffeomorphisms with the spectral metric.
An Ensemble Approach for Facial Expression Analysis in Video

Human emotions recognization contributes to the development of human-computer interaction. The machines understanding human emotions in the real world will significantly contribute to life in the future. This paper will introduce the Affective Behavior Analysis in-the-wild (ABAW3) 2022 challenge. The paper focuses on solving the problem of the valence-arousal estimation and action unit detection. For valence-arousal estimation, we conducted two stages: creating new features from multimodel and temporal learning to predict valence-arousal. First, we make new features; the Gated Recurrent Unit (GRU) and Transformer are combined using a Regular Networks (RegNet) feature, which is extracted from the image. The next step is the GRU combined with Local Attention to predict valence-arousal. The Concordance Correlation Coefficient (CCC) was used to evaluate the model.
Horizon-Free Reinforcement Learning in Polynomial Time: the Power of Stationary Policies

This paper gives the first polynomial-time algorithm for tabular Markov Decision Processes (MDP) that enjoys a regret bound \emph{independent on the planning horizon}. Specifically, we consider tabular MDP with $S$ states, $A$ actions, a planning horizon $H$, total reward bounded by $1$, and the agent plays for $K$ episodes. We design an algorithm that achieves an $O\left(\mathrm{poly}(S,A,\log K)\sqrt{K}\right)$ regret in contrast to existing bounds which either has an additional $\mathrm{polylog}(H)$ dependency~\citep{zhang2020reinforcement} or has an exponential dependency on $S$~\citep{li2021settling}. Our result relies on a sequence of new structural lemmas establishing the approximation power, stability, and concentration property of stationary policies, which can have applications in other problems related to Markov chains.
Lie trusses and heaps of Lie affebras

A frame-independent formulation of Lie brackets on affine spaces or {\em Lie affebras} introduced in [K. \ Grabowska, J.\ Grabowski \&P.\ Urba\'nski, Lie brackets on affine bundles, {\em Ann.\ Global Anal. \ Geom. } {\bf 24} (2003), 101--130] is given.
A comparison of two frameworks for multi-state modelling, applied to outcomes after hospital admissions with COVID-19

We compare two multi-state modelling frameworks that can be used to represent dates of events following hospital admission for people infected during an epidemic. The methods are applied to data from people admitted to hospital with COVID-19, to estimate the probability of admission to intensive care unit, the probability of death in hospital for patients before and after intensive care unit admission, the lengths of stay in hospital, and how all these vary with age and gender. One modelling framework is based on defining transition-specific hazard functions for competing risks. A less commonly used framework defines partially-latent subpopulations who will experience each subsequent event, and uses a mixture model to estimate the probability that an individual will experience each event, and the distribution of the time to the event given that it occurs. We compare the advantages and disadvantages of these two frameworks, in the context of the COVID-19 example. The issues include the interpretation of the model parameters, the computational efficiency of estimating the quantities of interest, implementation in software and assessing goodness of fit. In the example, we find that some groups appear to be at very low risk of some events, in particular intensive care unit admission, and these are best represented by using ‘cure-rate’ models to define transition-specific hazards. We provide general-purpose software to implement all the models we describe in the flexsurv R package, which allows arbitrarily flexible distributions to be used to represent the cause-specific hazards or times to events.
ZSM-based Management and Orchestration of 3GPP Network Slicing: An Architectural Framework and Deployment Options

Driven by closed-loop automation, the Zero-Touch Network and Services Management (ZSM) framework offers invigorating features for the end-to-end management and orchestration of a sliced network. Although ZSM is considered a promising framework by 3GPP, there is a lack of concrete ZSM-based solutions for the 3GPP Network Slicing management. This article presents an architectural framework of a ZSM-based management system for 3GPP Network Slicing. The proposed framework employs recursive ZSM management domains to meet the specific management requirements of the standard 3GPP Network Slicing framework. Two deployment options are considered for the presented framework. The first option features the integration of the standard services of the 3GPP Management System within ZSM. The other one considers ZSM as a complementary system for the standard 3GPP Management System. From these deployment options, some key questions are identified on ZSM's interoperability with the existing 3GPP Network Slicing systems. Finally, for each option, the architectural and operational feasibility of the ZSM interoperation with 3GPP Network Slicing systems is shown through an example use case.
Towards Escaping from Language Bias and OCR Error: Semantics-Centered Text Visual Question Answering

Texts in scene images convey critical information for scene understanding and reasoning. The abilities of reading and rea-soning matter for the model in the text-based visual question answering (TextVQA) process. However, current TextVQA models do not center on the text and suffer from several limitations. The model is easily dominated by language biases and optical character recognition (OCR) errors due to the ab-sence of semantic guidance in the answer prediction process. In this paper, we propose a novel Semantics-Centered Net-work (SC-Net) that consists of an instance-level contrastive semantic prediction module (ICSP) and a semantics-centered transformer module (SCT). Equipped with the two modules, the semantics-centered model can resist the language biases and the accumulated errors from OCR. Extensive experiments on TextVQA and ST-VQA datasets show the effectiveness of our model. SC- Net surpasses previous works with a notice-able margin and is more reasonable for the TextVQA task.
MD-SLAM: Multi-cue Direct SLAM

Simultaneous Localization and Mapping (SLAM) systems are fundamental building blocks for any autonomous robot navigating in unknown environments. The SLAM implementation heavily depends on the sensor modality employed on the mobile platform. For this reason, assumptions on the scene's structure are often made to maximize estimation accuracy. This paper presents a novel direct 3D SLAM pipeline that works independently for RGB-D and LiDAR sensors. Building upon prior work on multi-cue photometric frame-to-frame alignment [4], our proposed approach provides an easy-to-extend and generic SLAM system. Our pipeline requires only minor adaptations within the projection model to handle different sensor modalities. We couple a position tracking system with an appearance-based relocalization mechanism that handles large loop closures. Loop closures are validated by the same direct registration algorithm used for odometry estimation. We present comparative experiments with state-of-the-art approaches on publicly available benchmarks using RGB-D cameras and 3D LiDARs. Our system performs well in heterogeneous datasets compared to other sensor-specific methods while making no assumptions about the environment. Finally, we release an open-source C++ implementation of our system.
Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction

In this paper, we investigate improvements to the GEC sequence tagging architecture with a focus on ensembling of recent cutting-edge Transformer-based encoders in Large configurations. We encourage ensembling models by majority votes on span-level edits because this approach is tolerant to the model architecture and vocabulary size. Our best ensemble achieves a new SOTA result with an F_{0.5} score of 76.05 on BEA-2019 (test), even without pre-training on synthetic datasets. In addition, we perform knowledge distillation with a trained ensemble to generate new synthetic training datasets, “Troy-Blogs” and “Troy-1BW”. Our best single sequence tagging model that is pretrained on the generated Troy- datasets in combination with the publicly available synthetic PIE dataset achieves a near-SOTA result with an F_{0.5} score of 73.21 on BEA-2019 (test). The code, datasets, and trained models are publicly available.
Towards a deep learning model for hadronization

Hadronization is a complex quantum process whereby quarks and gluons become hadrons. The widely-used models of hadronization in event generators are based on physically-inspired phenomenological models with many free parameters. We propose an alternative approach whereby neural networks are used instead. Deep generative models are highly flexible, differentiable, and compatible with Graphical Processing Unit (GPUs). We make the first step towards a data-driven machine learning-based hadronization model by replacing a compont of the hadronization model within the Herwig event generator (cluster model) with a Generative Adversarial Network (GAN). We show that a GAN is capable of reproducing the kinematic properties of cluster decays. Furthermore, we integrate this model into Herwig to generate entire events that can be compared with the output of the public Herwig simulator as well as with $e^+e^-$ data.