Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation

Effectively measuring and modeling the reliability of a trained model is essential to the real-world deployment of monocular depth estimation (MDE) models. However, the intrinsic ill-posedness and ordinal-sensitive nature of MDE pose major challenges to the estimation of uncertainty degree of the trained models. On the one hand, utilizing current uncertainty modeling methods may increase memory consumption and are usually time-consuming. On the other hand, measuring the uncertainty based on model accuracy can also be problematic, where uncertainty reliability and prediction accuracy are not well decoupled. In this paper, we propose to model the uncertainty of MDE models from the perspective of the inherent probability distributions originating from the depth probability volume and its extensions, and to assess it more fairly with more comprehensive metrics. By simply introducing additional training regularization terms, our model, with surprisingly simple formations and without requiring extra modules or multiple inferences, can provide uncertainty estimations with state-of-the-art reliability, and can be further improved when combined with ensemble or sampling methods. A series of experiments demonstrate the effectiveness of our methods.
Criticality transition for positive powers of the discrete Laplacian on the half line

We study the criticality and subcriticality of powers $(-\Delta)^\alpha$ with $\alpha>0$ of the discrete Laplacian $-\Delta$ acting on $\ell^2(\mathbb{N})$. We prove that these positive powers of the Laplacian are critical if and only if $\alpha \ge 3/2$. We complement our analysis with Hardy type inequalities for $(-\Delta)^\alpha$ in the subcritical regimes $\alpha \in (0,3/2)$. As an illustration of the critical case, we describe the negative eigenvalues emerging by coupling the discrete bilaplacian with an arbitrarily small localized potential.
From Microscopic Interactions to Density Functionals

Density functional theory provides the basis for uncounted studies of ground-state properties of many-body systems. However, the connection between the energy density functional and the underlying microscopic interactions of a given theory is not fully understood. In this thesis we use renormalization-group techniques in combination with density functional theory to study many-body systems from microscopic interactions. We apply our formalism to different one-dimensional systems. We start with systems of identical fermions interacting via a short-range repulsive and long-range attractive interaction which serves as a simple one-dimensional toy model for nuclei. After that we study systems of spin-1/2 fermions, where we assume interactions only between fermions with different spins and consider both a non-local interaction as in the previous case and a contact interaction. In particular, the contact interaction plays a prominent role for ultracold Fermi gases. We calculate ground-state properties such as the energy, density, intrinsic density and density correlation functions and compare our results to values obtained from other approaches. Moreover, we show how energies of excited states and the absolute square of the ground-state wavefunction can be extracted from the density-density correlation function. The relation between our formalism and conventional density functional theory as well as many-body perturbation theory is discussed which may help to guide the development of ab initio functionals for quantitative studies of nuclei in the future.
DAPrompt: Deterministic Assumption Prompt Learning for Event Causality Identification

Event Causality Identification (ECI) aims at determining whether there is a causal relation between two event mentions. Conventional prompt learning designs a prompt template to first predict an answer word and then maps it to the final decision. Unlike conventional prompts, we argue that predicting an answer word may not be a necessary prerequisite for the ECI task. Instead, we can first make a deterministic assumption on the existence of causal relation between two events and then evaluate its rationality to either accept or reject the assumption. The design motivation is to try the most utilization of the encyclopedia-like knowledge embedded in a pre-trained language model. In light of such considerations, we propose a deterministic assumption prompt learning model, called DAPrompt, for the ECI task. In particular, we design a simple deterministic assumption template concatenating with the input event pair, which includes two masks as predicted events' tokens. We use the probabilities of predicted events to evaluate the assumption rationality for the final event causality decision. Experiments on the EventStoryLine corpus and Causal-TimeBank corpus validate our design objective in terms of significant performance improvements over the state-of-the-art algorithms.
Divert More Attention to Vision-Language Object Tracking

Multimodal vision-language (VL) learning has noticeably pushed the tendency toward generic intelligence owing to emerging large foundation models. However, tracking, as a fundamental vision problem, surprisingly enjoys less bonus from recent flourishing VL learning. We argue that the reasons are two-fold: the lack of large-scale vision-language annotated videos and ineffective vision-language interaction learning of current works. These nuisances motivate us to design more effective vision-language representation for tracking, meanwhile constructing a large database with language annotation for model learning. Particularly, in this paper, we first propose a general attribute annotation strategy to decorate videos in six popular tracking benchmarks, which contributes a large-scale vision-language tracking database with more than 23,000 videos. We then introduce a novel framework to improve tracking by learning a unified-adaptive VL representation, where the cores are the proposed asymmetric architecture search and modality mixer (ModaMixer). To further improve VL representation, we introduce a contrastive loss to align different modalities. To thoroughly evidence the effectiveness of our method, we integrate the proposed framework on three tracking methods with different designs, i.e., the CNN-based SiamCAR, the Transformer-based OSTrack, and the hybrid structure TransT. The experiments demonstrate that our framework can significantly improve all baselines on six benchmarks. Besides empirical results, we theoretically analyze our approach to show its rationality. By revealing the potential of VL representation, we expect the community to divert more attention to VL tracking and hope to open more possibilities for future tracking with diversified multimodal messages.
Fine-grained Text-Video Retrieval with Frozen Image Encoders

State-of-the-art text-video retrieval (TVR) methods typically utilize CLIP and cosine similarity for efficient retrieval. Meanwhile, cross attention methods, which employ a transformer decoder to compute attention between each text query and all frames in a video, offer a more comprehensive interaction between text and videos. However, these methods lack important fine-grained spatial information as they directly compute attention between text and video-level tokens. To address this issue, we propose CrossTVR, a two-stage text-video retrieval architecture. In the first stage, we leverage existing TVR methods with cosine similarity network for efficient text/video candidate selection. In the second stage, we propose a novel decoupled video text cross attention module to capture fine-grained multimodal information in spatial and temporal dimensions. Additionally, we employ the frozen CLIP model strategy in fine-grained retrieval, enabling scalability to larger pre-trained vision models like ViT-G, resulting in improved retrieval performance. Experiments on text video retrieval datasets demonstrate the effectiveness and scalability of our proposed CrossTVR compared to state-of-the-art approaches.
Multi-View Self-Supervised Learning For Multivariate Variable-Channel Time Series

Labeling of multivariate biomedical time series data is a laborious and expensive process. Self-supervised contrastive learning alleviates the need for large, labeled datasets through pretraining on unlabeled data. However, for multivariate time series data, the set of input channels often varies between applications, and most existing work does not allow for transfer between datasets with different sets of input channels. We propose learning one encoder to operate on all input channels individually. We then use a message passing neural network to extract a single representation across channels. We demonstrate the potential of this method by pretraining our model on a dataset with six EEG channels and then fine-tuning it on a dataset with two different EEG channels. We compare models with and without the message passing neural network across different contrastive loss functions. We show that our method, combined with the TS2Vec loss, outperforms all other methods in most settings.
Reconstructing the Volume Density Profile of Prestellar Cores with the Anisotropic Inverse Abel Transform Method

Most studies of the 3D density structure of prestellar cores have used isotropic evolution models, which are projected into 2D for direct comparison with observation data. In this study, we introduced the anisotropic inverse Abel transform method for reconstructing the 3D volume density profile of prestellar cores. We demonstrated that this approach could accurately preserve information about the realistic 3D structure with numerical simulation. This method has great advantages compared to the shape-assumption methods, particularly in high-density regions. We applied this method to the Herschel 2D column density maps of three prestellar cores, and the results showed good agreement with theoretical models. By changing the direction of the inversion axis of symmetry, we confirmed that the density profiles of prestellar cores exhibit nearly cylindrical symmetry.
Optimal Kernel-Based Dynamic Mode Decomposition

The state-of-the-art algorithm known as kernel-based dynamic mode decomposition (K-DMD) provides a sub-optimal solution to the problem of reduced modeling of a dynamical system based on a finite approximation of the Koopman operator. It relies on crude approximations and on restrictive assumptions. The purpose of this work is to propose a kernel-based algorithm solving exactly this low-rank approximation problem in a general setting.
Irreversible Markov chains by the factorized Metropolis filter : algorithms and applications in particle systems and spin models

This thesis deals with the development and application in statistical physics of a general framework for irreversible and rejection-free Markov-chain Monte Carlo methods, through the implementation of the factorized Metropolis filter and the lifting concept. The first two chapters present the Markov-chain Monte Carlo method and its different implementations in statistical physics. One of the main limitations of Markov-chain Monte Carlo methods arises around phase transitions, where phenomena of dynamical slowing down greatly impede the thermalization of the system. The third chapter introduces the new class of irreversible factorized Metropolis algorithms. Building on the concept of lifting of Markov chains, the factorized Metropolis filter allows to decompose a multidimensional potential into several unidimensional ones. From there, it is possible to define a rejection-free and completely irreversible Markov-chain Monte Carlo algorithm. The fourth chapter reviews the performance of the irreversible factorized algorithm in a wide variety of systems. Clear accelerations of the thermalization time are observed in bidimensional soft-particle systems, bidimensional ferromagnetic XY spin systems and three-dimensional XY spin glasses. Finally, an important reduction of the critical slowing down is exhibited in three-dimensional ferromagnetic Heisenberg spin systems.
S100A9 is indispensable for survival of pneumococcal pneumonia in mice

S100A8/A9 has important immunomodulatory roles in antibacterial defense, but its relevance in focal pneumonia caused by Streptococcus pneumoniae (S. pneumoniae) is understudied. We show that S100A9 was significantly increased in BAL fluids of patients with bacterial but not viral pneumonia and correlated with procalcitonin and sequential organ failure assessment scores. Mice deficient in S100A9 exhibited drastically elevated Zn2+ levels in lungs, which led to bacterial outgrowth and significantly reduced survival. In addition, reduced survival of S100A9 KO mice was characterized by excessive release of neutrophil elastase, which resulted in degradation of opsonophagocytically important collectins surfactant proteins A and D. All of these features were attenuated in S. pneumoniae-challenged chimeric WT→S100A9 KO mice. Similarly, therapy of S. pneumoniae-infected S100A9 KO mice with a mutant S100A8/A9 protein showing increased half-life significantly decreased lung bacterial loads and lung injury. Collectively, S100A9 controls central antibacterial immune mechanisms of the lung with essential relevance to survival of pneumococcal pneumonia. Moreover, S100A9 appears to be a promising biomarker to distinguish patients with bacterial from those with viral pneumonia. Trial registration: Clinical Trials register (DRKS00000620).
Counter-stereotypical messaging and partisan cues: Moving the needle on vaccines in a polarized United States

This paper reports results from a large-scale randomized controlled trial assessing whether counter-stereotypical messaging and partisan cues can induce people to get COVID-19 vaccines. The study used a 27-s video compilation of Donald Trump’s comments about the vaccine from Fox News interviews and presented the video to millions of U.S. YouTube users through a $100,000 advertising campaign in October 2021. Results indicate that the number of vaccines increased in the average treated county by 103 (with a one-tailed P value of 0.097). Based on this average treatment effect and totaling across our 1014 treated counties, the total estimated effect was 104,036 vaccines.
Interindividual variability in the benefits of personal sound amplification products on speech perception in noise: A randomized cross-over clinical trial

Objective The aging population is prone to hearing loss, which has several adverse effects on quality of life, including difficulty following conversations in noisy environments. Personal Sound Amplification Products (PSAPs) are a less expensive, over-the-counter alternative to traditional, more expensive hearing aids. Although some studies have shown that PSAPs can mitigate hearing loss, the literature generally only addresses group differences without considering interindividual variability. This study aimed to 1) determine how PSAPs affect listening effort and speech perception in noise and 2) measure interindividual variability and identify contributing demographic and health factors. Design We used a cross-over design in which all participants were assigned to each condition. Participants Twenty-eight adults aged 60 to 87 years with normal hearing and mild hearing loss fulfilled the study requirements. Intervention In one session, speech-in-noise perception tasks were performed without PSAPs, and in the other, the tasks were performed with bilateral PSAPs. The two sessions were separated by one week, and the order of the sessions was balanced across participants. Main outcome measures In both sessions, participants performed the Quick speech-in-noise test and a word discrimination task in noise, in which their self-reported listening effort was measured. Results PSAPs use improved speech perception in noise in both tasks and reduced listening effort. There was considerable variability between individuals, with approximately 60–70% of participants showing benefit. Age, hearing and cognitive status were significant predictors of the benefits. Conclusion Not all individuals may benefit from the effect of PSAPs to the same extent at their first use, and this depends on specific health and demographic factors, particularly age, hearing, and cognitive status. These results underscore the importance of demographic and health factors in assessing the benefits of hearing amplification in older adults. Trial registration ClinicalTrials.gov, NCT05076045.
Association between an inflammatory biomarker score and future dementia diagnosis in the population-based UK Biobank cohort of 500,000 people

This study was designed to investigate the relationship between a systematic inflammatory biomarker measure, concurrent and later cognitive performance, and future dementia risk. The literature has reported the potential involvement of inflammation in cognitive performance as well as Alzheimer’s Disease, but not consistently. We used a population-based cohort of 500,000 people in the UK and assessed the association between a composite inflammatory biomarker and cognitive performance measures across five domains measured concurrently and 4–13 years later, taking advantage of the large sample size. We also assessed the same biomarker’s association with dementia diagnosis 3–11 years later in the initially dementia-free sample. We report small but significant associations between elevated biomarker levels and worsened cognitive performance at baseline for four cognitive tasks (OR = 1.204, p<0.001 for Prospective memory, β = -0.366, p<0.001 for Fluid intelligence, β = 8.819, p<0.001 for Reaction time, and β = -0.224, p<0.001 for Numeric memory), comparing the highest quartile of the biomarker to the lowest. We also found that for one measure (Pairs matching) higher biomarker levels were associated with fewer errors, i.e. better performance (β = -0.096, p<0.001). We also report that the 4th quartiles of the baseline biomarker levels were significantly associated with cognitive task scores assessed years later on the p< = 0.002 level, except for the Pair matching test, for which none of the quartiles remained a significant predictor. Finally, the highest biomarker quartile was significantly associated with increased dementia risk compared to the lowest quartile (HR = 1.349, p<0.001). A case-only analysis to assess disease subtype heterogeneity suggested probable differences in the association with the highest biomarker quartile between vascular dementia and Alzheimer disease subtypes (OR = 1.483, p = 0.055). Our results indicate that systemic inflammation may play a small but significant part in dementia pathophysiology, especially in vascular dementia.
The effect of entrepreneurship education on the entrepreneurial intention of different college students: Gender, household registration, school type, and poverty status

Entrepreneurship education (EE) is a crucial link to promoting college students’ entrepreneurship, which reduces unemployment, economic development, and poverty. Based on a sample of Chinese college students, this study uses a logistic model to investigate the effect of EE on entrepreneurial intention (EI). It focuses on evaluating the impact of EE on the EI of different groups of college students from the perspectives of gender, household registration, school type, and poverty status. Benchmark regression results show that EE has a significant positive impact on the EI of students. The heterogeneity analysis has the following findings. First, EE has a more significant impact on women’s EI, which can reduce the entrepreneurial gap between women and men. Second, EE is more effective in improving the EI of urban students, which will further widen the gap between urban and rural students in entrepreneurship. Third, EE has increased the EI of students from private universities, which represent application-oriented universities. This shows that public universities, which represent research-oriented universities, need to increase the training of talent in practical applications to narrow the gap with private universities in entrepreneurship. Fourth, after receiving EE, the EI of nonpoor students improved more than that of poor students. Equal EE increases the gap between poor and nonpoor students, which can easily lead to an intergenerational cycle of poverty in entrepreneurship. This study provides empirical evidence from college students’ entrepreneurship in relatively underdeveloped western China, which supports the development of EE and entrepreneurial activities.
Applying an Extended Protection Motivation Theory Model to Predict Resident Hospitality During the COVID-19 Crisis

This study integrates trust in government, fear of COVID-19, and economic dependence on tourism into the protection motivation theory (PMT) framework to predict resident hospitality during the COVID-19 crisis. Structural equation modeling is utilized to analyze 281 questionnaires from destination residents. Findings show that threat severity and threat vulnerability can indirectly reduce resident hospitality through fear of COVID-19, whereas response efficacy and self-efficacy can directly promote resident hospitality. Trust in government can change the levels of threat severity, threat vulnerability, response efficacy, and self-efficacy, which in turn influences fear of COVID-19 and resident hospitality in different ways. Additionally, economic dependence on tourism is found as the most influential predictor of resident hospitality. Theoretically, this study contributes to the literature by developing a comprehensive and novel extended PMT model to understand the formation mechanism of residents’ attitudes during the COVID-19 crisis. Practical implications can help better improve resident hospitality.
Theoretical and numerical study of a few stochastic models of statistical physics

In this thesis, we are mainly interested in three topics : functional inequalities and their probabilistic aspects, hydrodynamic limits for interacting continuous spin systems and discretizations of stochastic differential equations. This document, in addition to a general introduction (written in French), contains three parts. The first part deals with functional inequalities, especially logarithmic Sobolev inequalities, for canonical ensembles, and with hydrodynamic limits for continuous spin systems. We prove convergence to the hydrodynamic limit for several variants of the Ginzburg--Landau model endowed with Kawasaki dynamics, with quantitative bounds in the number of spins. We also study convergence of the microscopic entropy to its hydrodynamic counterpart. In the second part, we study links between gradient flows in spaces of probability measures and large deviations for sequences of laws of solutions to stochastic differential equations. We show that the large deviations principle is equivalent to the Gamma--Convergence of a sequence of functionals that appear in the gradient flow formulation of the flow of marginals of the laws of the diffusion processes. As an application of this principle, we obtain large deviations from the hydrodynamic limit for two variants of the Ginzburg-Landau model. The third part deals with the discretization of stochastic differential equations. We prove a transport-Entropy inequality for the law of the explicit Euler scheme. This inequality implies bounds on the confidence intervals for quantities of the form $\mathbb{E}[f(X_T)]$. We also study the discretization error for the evaluation of transport coefficients with the Metropolis-Adjusted Langevin algorithm (which is a combination of the explicit Euler scheme and the Metropolis algorithm).