Miracle-dz/UniFAR-FTUs huggingface dataset:
Feature-compatible Progressive Learning for Video Copy Detection

Video Copy Detection (VCD) has been developed to identify instances of unauthorized or duplicated video content. This paper presents our second place solutions to the Meta AI Video Similarity Challenge (VSC22), CVPR 2023. In order to compete in this challenge, we propose Feature-Compatible Progressive Learning (FCPL) for VCD. FCPL trains various models that produce mutually-compatible features, meaning that the features derived from multiple distinct models can be directly compared with one another. We find this mutual compatibility enables feature ensemble. By implementing progressive learning and utilizing labeled ground truth pairs, we effectively gradually enhance performance. Experimental results demonstrate the superiority of the proposed FCPL over other competitors. Our code is available at https://github.com/WangWenhao0716/VSC-DescriptorTrack-Submission and https://github.com/WangWenhao0716/VSC-MatchingTrack-Submission.
LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields

We introduce a new task, novel view synthesis for LiDAR sensors. While traditional model-based LiDAR simulators with style-transfer neural networks can be applied to render novel views, they fall short of producing accurate and realistic LiDAR patterns because the renderers rely on explicit 3D reconstruction and exploit game engines, that ignore important attributes of LiDAR points. We address this challenge by formulating, to the best of our knowledge, the first differentiable end-to-end LiDAR rendering framework, LiDAR-NeRF, leveraging a neural radiance field (NeRF) to facilitate the joint learning of geometry and the attributes of 3D points. However, simply employing NeRF cannot achieve satisfactory results, as it only focuses on learning individual pixels while ignoring local information, especially at low texture areas, resulting in poor geometry. To this end, we have taken steps to address this issue by introducing a structural regularization method to preserve local structural details. To evaluate the effectiveness of our approach, we establish an object-centric multi-view LiDAR dataset, dubbed NeRF-MVL. It contains observations of objects from 9 categories seen from 360-degree viewpoints captured with multiple LiDAR sensors. Our extensive experiments on the scene-level KITTI-360 dataset, and on our object-level NeRF-MVL show that our LiDAR-NeRF surpasses the model-based algorithms significantly.
Galaxy morphology classification using multiscale convolution capsule network

Classification of galaxy morphology is a hot issue in astronomical research. Although significant progress has been made in the last decade in classifying galaxy morphology using deep learning technology, there are still some deficiencies in spatial feature representation and classification accuracy. In this study, we present a multiscale convolutional capsule network (MSCCN) model for the classification of galaxy morphology. First, this model improves the convolutional layers using a multibranch structure to extract the multiscale hidden features of galaxy images. In order to further explore the hidden information in the features, the multiscale features are encapsulated and fed into the capsule layer. Second, we use a sigmoid function to replace the softmax function in dynamic routing, which can enhance the robustness of MSCCN. Finally, the classification model achieves 97 per cent accuracy, 96 per cent precision, 98 per cent recall, and 97 per cent F1-score under macroscopic averaging. In addition, a more comprehensive model evaluation was accomplished in this study. We visualized the morphological features for the part of sample set that used the t-distributed stochastic neighbour embedding (t-SNE) algorithm. The results show that the model has a better generalization ability and robustness, and it can be effectively used in the galaxy morphological classification.
Generalized marginal cumulative logistic model for multi-way contingency tables

For multi-way contingency tables of same classifications with or- dered categories, this paper proposes two generalized marginal cumulative logis- tic models. Those indicate that the difference between two marginal cumulative logits, or two conditional marginal cumulative logits on condition that all val- ues of the variables are not identical, is a polynomial function of the category value. This paper also gives a theorem that the marginal homogeneity model holds if and only if both the proposed model and moment equality model hold. An example is given. AMS 2010 Mathematics Subject Classication. 62H17.
Corrections to the Bethe lattice solution of Anderson localization

We study numerically Anderson localization on lattices that are tree-like except for the presence of one loop of varying length $L$. The resulting expressions allow us to compute corrections to the Bethe lattice solution on i) Random-Regular-Graph (RRG) of finite size $N$ and ii) euclidean lattices in finite dimension. In the first case we show that the $1/N$ corrections to to the average values of observables such as the typical density of states and the inverse participation ratio have prefactors that diverge exponentially approaching the critical point, which explains the puzzling observation that the numerical simulations on finite RRGs deviate spectacularly from the expected asymptotic behavior. In the second case our results, combined with the $M$-layer expansion, predict that corrections destroy the exotic critical behavior of the Bethe lattice solution in any finite dimension, strengthening the suggestion that the upper critical dimension of Anderson localization is infinity. This approach opens the way to the computation of non-mean-field critical exponents by resumming the series of diverging diagrams through the same recipes of the field-theoretical perturbative expansion.
DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits Animation

Talking head synthesis is a promising approach for the video production industry. Recently, a lot of effort has been devoted in this research area to improve the generation quality or enhance the model generalization. However, there are few works able to address both issues simultaneously, which is essential for practical applications. To this end, in this paper, we turn attention to the emerging powerful Latent Diffusion Models, and model the Talking head generation as an audio-driven temporally coherent denoising process (DiffTalk). More specifically, instead of employing audio signals as the single driving factor, we investigate the control mechanism of the talking face, and incorporate reference face images and landmarks as conditions for personality-aware generalized synthesis. In this way, the proposed DiffTalk is capable of producing high-quality talking head videos in synchronization with the source audio, and more importantly, it can be naturally generalized across different identities without further finetuning. Additionally, our DiffTalk can be gracefully tailored for higher-resolution synthesis with negligible extra computational cost. Extensive experiments show that the proposed DiffTalk efficiently synthesizes high-fidelity audio-driven talking head videos for generalized novel identities. For more video results, please refer to https://sstzal.github. io/DiffTalk/.
Bridging Natural Language Processing and Psycholinguistics: computationally grounded semantic similarity datasets for Basque and Spanish

We present a computationally-grounded word similarity dataset based on two well-known Natural Language Processing resources; text corpora and knowledge bases. This dataset aims to fulfil a gap in psycholinguistic research by providing a variety of quantifications of semantic similarity in an extensive set of noun pairs controlled by variables that play a significant role in lexical processing. The dataset creation has consisted in three steps, 1) computing four key psycholinguistic features for each noun; concreteness, frequency, semantic and phonological neighbourhood density; 2) pairing nouns across these four variables; 3) for each noun pair, assigning three types of word similarity measurements, computed out of text, Wordnet and hybrid embeddings. The present dataset includes noun pairs' information in Basque and European Spanish, but further work intends to extend it to more languages.
Inverse transport problem solvers based on regularized and compressive sensing techniques

According to the direct exposure measurements from flash radiographic image, regularized-based method and compressive sensing (CS)-based method for inverse transport equation are presented. The linear absorption coefficients and interface locations of objects are reconstructed directly at the same time. With a large number of measurements, least-square method is utilized to complete the reconstruction. Owing to the ill-posedness of the inverse problems, regularized algorithm is employed. Tikhonov method is applied with an appropriate posterior regularization parameter to get a meaningful solution. However, it's always very costly to obtain enough measurements. With limited measurements, CS sparse reconstruction technique Orthogonal Matching Pursuit (OMP) is applied to obtain the sparse coefficients by solving an optimization problem. This paper constructs and takes the forward projection matrix rather than Gauss matrix as measurement matrix. In the CS-based algorithm, Fourier expansion and wavelet expansion are adopted to convert an underdetermined system to a well-posed system. Simulations and numerical results of regularized method with appropriate regularization parameter and that of CS-based agree well with the reference value, furthermore, both methods avoid amplifying the noise. (authors)
Projective Proximal Gradient Descent for A Class of Nonconvex Nonsmooth Optimization Problems: Fast Convergence Without Kurdyka-Lojasiewicz (KL) Property

Nonconvex and nonsmooth optimization problems are important and challenging for statistics and machine learning. In this paper, we propose Projected Proximal Gradient Descent (PPGD) which solves a class of nonconvex and nonsmooth optimization problems, where the nonconvexity and nonsmoothness come from a nonsmooth regularization term which is nonconvex but piecewise convex. In contrast with existing convergence analysis of accelerated PGD methods for nonconvex and nonsmooth problems based on the Kurdyka-\L{}ojasiewicz (K\L{}) property, we provide a new theoretical analysis showing local fast convergence of PPGD. It is proved that PPGD achieves a fast convergence rate of $\cO(1/k^2)$ when the iteration number $k \ge k_0$ for a finite $k_0$ on a class of nonconvex and nonsmooth problems under mild assumptions, which is locally Nesterov's optimal convergence rate of first-order methods on smooth and convex objective function with Lipschitz continuous gradient. Experimental results demonstrate the effectiveness of PPGD.
A pr 2 02 3 Causal influence , causal effects , and path analysis in the presence of intermediate confounding

Recent approaches to causal inference have focused on the identification and estimation of causal effects, defined as (properties of) the distribution of counterfactual outcomes under hypothetical actions that alter the nodes of a graphical model. In this article we explore an alternative approach using the concept of causal influence, defined through operations that alter the information propagated through the edges of a directed acyclic graph. Causal influence may be more useful than causal effects in settings in which interventions on the causal agents are infeasible or of no substantive interest, for example when considering gender, race, or genetics as a causal agent. Furthermore, the “information transfer” interventions proposed allow us to solve a long-standing problem in causal mediation analysis, namely the non-parametric identification of path-specific effects in the presence of treatment-induced mediator-outcome confounding. We propose efficient non-parametric estimators for a covariance version of the proposed causal influence measures, using data-adaptive regression coupled with semi-parametric efficiency theory to address model misspecification bias while retaining √ n-consistency and asymptotic normality. We illustrate the use of our methods in two examples using publicly available data. *corresponding author: ild2005@med.cornell. edu
Developing Gender-Affirming Surgery Curricula for Plastic Surgery Residency and Fellowship via Delphi Consensus

Background: Gender-affirming surgery (GAS) is a rapidly growing field within plastic surgery, and residents and fellows must receive appropriate training. However, there are no standardized surgical training curricula. The objective of this study was to identify core curricula within the field of GAS. Methods: Four GAS surgeons from different academic institutions identified initial curricular statements within six categories: (1) comprehensive GAS care, (2) gender-affirming facial surgery, (3) masculinizing chest surgery, (4) feminizing breast augmentation, (5) masculinizing genital GAS, and (6) feminizing genital GAS. Expert panelists consisting of plastic surgery residency program directors and GAS surgeons were recruited for three rounds of the Delphi-consensus process. The panelists decided whether each curriculum statement was appropriate for residency, fellowship, or neither. A statement was included in the final curriculum when Cronbach α value was greater than or equal to 0.8, meaning that 80% or more of the panel agreed on inclusion. Results: A total of 34 panelists (14 plastic surgery residency program directors and 20 GAS surgeons representing 28 US institutions) participated. The response rate was 85% for the first round, 94% for the second, and 100% for the third. Out of 124 initial curriculum statements, 84 reached consensus for the final GAS curricula, 51 for residency, and 31 for fellowship. Conclusions: A national consensus on core GAS curriculum for plastic surgery residency and GAS fellowship was achieved by a modified Delphi method. Implementation of this curriculum will ensure that trainees in plastic surgery are adequately prepared in the field of GAS.
Endosidin 5 disruption of the Golgi apparatus and extracellular matrix secretion in the unicellular charophyte Penium margaritaceum

Abstract Background and Aims Endosidins are a group of low-molecular-weight compounds, first identified by ‘chemical biology’ screening assays, that have been used to target specific components of the endomembrane system. In this study, we employed multiple microscopy-based screening techniques to elucidate the effects of endosidin 5 (ES5) on the Golgi apparatus and the secretion of extracellular matrix (ECM) components in Penium margaritaceum. These effects were compared with those caused by treatments with brefeldin A and concanamycin A. Penium margaritaceum’s extensive Golgi apparatus and endomembrane system make it an outstanding model organism for screening changes to the endomembrane system. Here we detail changes to the Golgi apparatus and secretion of ECM material caused by ES5. Methods Changes to extracellular polymeric substance (EPS) secretion and cell wall expansion were screened using fluorescence microscopy. Confocal laser scanning microscopy and transmission electron microscopy were used to assess changes to the Golgi apparatus, the cell wall and the vesicular network. Electron tomography was also performed to detail the changes to the Golgi apparatus. Key Results While other endosidins were able to impact EPS secretion and cell wall expansion, only ES5 completely inhibited EPS secretion and cell wall expansion over 24 h. Short treatments of ES5 resulted in displacement of the Golgi bodies from their typical linear alignment. The number of cisternae decreased per Golgi stack and trans face cisternae in-curled to form distinct elongate circular profiles. Longer treatment resulted in a transformation of the Golgi body to an irregular aggregate of cisternae. These alterations could be reversed by removal of ES5 and returning cells to culture. Conclusions ES5 alters secretion of ECM material in Penium by affecting the Golgi apparatus and does so in a markedly different way from other endomembrane inhibitors such as brefeldin A and concanamycin A.
Age as a moderating factor of treatment resistance in depression

Abstract Background Treatment-resistant depression (TRD) is an important clinical challenge and may present differently between age groups. Methods A total of 893 depressed patients recruited within the framework of the European research consortium “Group for the Studies of Resistant Depression” were assessed by generalized linear models regarding age effects (both as numerical and factorial predictors) on treatment outcome, number of lifetime depressive episodes, hospitalization time, and duration of the current episode. Effects of age as numerical predictor on the severity of common depressive symptoms, measured with Montgomery–Åsberg Depression Rating Scale (MADRS) for two-time points, were assessed by linear mixed models, respectively, for patients showing TRD and treatment response. A corrected p threshold of 0.001 was applied. Results Overall symptom load reflected by MADRS (p < 0.0001) and lifetime hospitalization time (p < 0.0001) increased with age in TRD patients but not treatment responders. In TRD, higher age was predicting symptom severity of inner tension, reduced appetite, concentrations difficulties, and lassitude (all p ≤ 0.001). Regarding clinical significance, older TRD patients were more likely to report severe symptoms (item score > 4) for these items both before and after treatment (all p ≤ 0.001). Conclusions In this naturalistic sample of severely ill depressed patients, antidepressant treatment protocols were equally effective in addressing TRD in old age. However, specific symptoms such as sadness, appetite, and concentration showed an age-dependent presentation, impacting residual symptoms in severely affected TRD patients and calling for a precision approach by a better integration of age profiles in treatment recommendations.
An Information-Theoretic Method to Automatic Shortcut Avoidance and Domain Generalization for Dense Prediction Tasks

Deep convolutional neural networks for dense prediction tasks are commonly optimized using synthetic data, as generating pixel-wise annotations for real-world data is laborious. However, the synthetically trained models do not generalize well to real-world environments. This poor “synthetic to real” (S2R) generalization we address through the lens of shortcut learning. We demonstrate that the learning of feature representations in deep convolutional networks is heavily influenced by synthetic data artifacts (shortcut attributes). To mitigate this issue, we propose an Information-Theoretic Shortcut Avoidance (ITSA) approach to automatically restrict shortcut-related information from being encoded into the feature representations. Specifically, our proposed method minimizes the sensitivity of latent features to input variations: to regularize the learning of robust and shortcut-invariant features in synthetically trained models. To avoid the prohibitive computational cost of direct input sensitivity optimization, we propose a practical yet feasible algorithm to achieve robustness. Our results show that the proposed method can effectively improve S2R generalization in multiple distinct dense prediction tasks, such as stereo matching, optical flow, and semantic segmentation. Importantly, the proposed method enhances the robustness of the synthetically trained networks and outperforms their fine-tuned counterparts (on real data) for challenging out-of-domain applications.
Energy evolution and crack development characteristics of sandstone under freeze-thaw cycles by digital image correlation

Freeze-thaw erosion is the main reason for rock mass instability in cold regions and poses major threats to public safety. In this study, the stress threshold, energy, and strain field evolution of sandstone and the variation in stress intensity factor of fractures in various stress fields were all investigated after freeze-thaw cycles by uniaxial compression tests and digital image correlation technology. The results show that the elastic modulus, crack initiation stress, and peak stress all fell by 97%, 92.5%, and 89.9%, respectively, as the number of freeze-thaw cycles approaches 80. Elastic energy’s storage capacity also dropped from 0.85 to 0.17. Sandstone’s strain was increased by freeze-thaw erosion, which also improved ductility and shortened the cracking time. The stress intensity factor at the crack tip was positively correlated with the tip inclination angle and negatively correlated with the number of freeze-thaw cycles. This study provides a useful reference for understanding the stability of rock masses and the characteristics of crack derivation in cold regions.
Toll-Like Receptor 9 Signaling Pathway Contributes to Intestinal Mucosal Barrier Injury in Mice With Severe Acute Pancreatitis

Objectives The purpose of this study was to investigate the role and mechanism of toll-like receptor 9 (TLR9) in intestinal mucosal barrier injury in mice with severe acute pancreatitis (SAP). Methods The mice were randomly divided into 3 groups: control group, SAP group, and TLR9 antagonist–treated group. The expression of tumor necrosis factor-α, interleukin-1β, interleukin-6, diamine oxidase, and endotoxin core antibodies were detected by enzyme-linked immunosorbent assay. The protein expression of zonula occluden-1 (ZO)-1, occludin, TLR9, myeloid differentiation factor 88 (MyD88), tumor necrosis factor receptor–associated factor 6 (TRAF6), p-nuclear factor (NF)-κB p65, and NF-κB p65 were detected by Western blot. TdT-mediated dUTP nick-end labeling staining was used for detecting intestinal epithelial cell apoptosis. Results The expression of TLR9 and its related pathway proteins MyD88, TRAF6, and p-NF-κB p65 in the intestinal tract of SAP mice were significantly increased compared with that of control mice. Inhibition of the TLR9 expression could reduce the level of serum proinflammatory cytokines, reduce the apoptosis of intestinal epithelial cells, improve intestinal permeability, and ultimately reduce the damage of intestinal mucosal barrier function in SAP. Conclusions Toll-like receptor 9/MyD88/TRAF6/NF-κB signaling pathway plays an important role in intestinal mucosal barrier injury of SAP.
Using quantitative magnetic resonance imaging to track cerebral alterations in multiple sclerosis brain: A longitudinal study

Quantitative MRI quantifies tissue microstructural properties and supports the characterization of cerebral tissue damages. With an MPM protocol, 4 parameter maps are constructed: MTsat, PD, R1 and R2*, reflecting tissue physical properties associated with iron and myelin contents. Thus, qMRI is a good candidate for in vivo monitoring of cerebral damage and repair mechanisms related to MS. Here, we used qMRI to investigate the longitudinal microstructural changes in MS brain.