Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of environmental awareness and conditions on successful aging: Evidence of air and water pollution in China

ABSTRACT This study examines three aspects of successful aging including subjective health, cognitive ability, and social participation and their relations with environmental conditions and environmental awareness. We used the seemingly uncorrelated regression and multilevel models to estimate the joint relationship by combining the Chinese General Social Survey (2013 and 2021) (N = 5404 and 1580 accordingly) and the Chinese Statistical Yearbook and used propensity score analysis to identify potential endogenous issues. The first finding is that older people who were men, married, lived in urban areas, or received social security had an edge in aging successfully. Secondly, the adverse effects of environmental pollution on older adults’ cognitive ability and social participation can be reduced by the protective effects of environmental awareness. Additionally, air pollution had more apparent effects, and water pollution was more modest in social involvement. This study offers empirical results to enhance the well-being of the older population by lifting their environmental perception and improving environmental quality.
Granulomatous mural folliculitis in 16 domestic goats: Infection with malignant catarrhal fever viruses and colocalization with ovine herpesvirus-2 using in situ hybridization

Granulomatous mural folliculitis (GMF) is an uncommon reaction pattern occasionally observed in nonadapted ruminant hosts infected with malignant catarrhal fever viruses. This report characterizes GMF and concurrent cutaneous lesions in 16 goats with crusting dermatitis using histochemistry including hematoxylin and eosin, periodic acid–Schiff, and Grocott’s methenamine silver, and immunohistochemistry for CD3, CD20, ionized calcium binding adaptor molecule 1, and cytokeratin AE1/3. Infiltrates in all 16 GMF cases consisted of macrophages and fewer T lymphocytes, and variably included eosinophils, multinucleated histiocytic giant cells, and/or neutrophils. Formalin-fixed paraffin-embedded skin and fresh skin samples from caprine GMF cases were tested using pan-herpesvirus nested conventional polymerase chain reaction (PCR) and partial sequencing, ovine herpesvirus-2 (OvHV-2) real-time PCR, and OvHV-2 colorimetric in situ hybridization (ISH). Five of 16 goats with GMF (31%) were PCR positive for malignant catarrhal fever viruses, including caprine herpesvirus 3 in 1 goat and OvHV-2 in 4 goats. Three goats also had positive intranuclear OvHV-2 hybridization signal in follicular keratinocytes, among other cell types, localized to areas of GMF. Herpesviruses were not detected in the formalin-fixed paraffin-embedded skin of 9 goats without GMF. This case series describes relatively frequent detections of malignant catarrhal fever viruses in the skin of goats with GMF, including the first report of caprine herpesvirus 3, and localizes OvHV-2 infected follicular keratinocytes within areas of GMF.
Thermionic energy conversion based on 3D Dirac semimetals

First, we investigate theoretically the thermionic emission from three-dimensional Dirac semimetals (3D DSs), then we explore their practical application as the cathode of a thermionic energy generator (TIEG). Based on the linear energy dispersion around Dirac points, we derive the generalized analytical formula of the thermionic emission from 3D DSs through the Dirac Hamilton, which is significantly different from the Richardson–Dushman (RD) law for metallic materials. The reason for this originates from the fact that electrons behave like massless fermions and follow ultrarelativistic quasiparticle dynamics in 3D DSs, deviating from the nonrelativistic electrons with a parabolic energy dispersion in conventional metal materials. We develop a theoretical model of the solid-state thermionic energy generator using 3D DSs as the cathode and obtain its optimal working regions at different temperatures. For a 3D DS-based cathode at 1400 K, the maximum efficiency of the proposed TIEG can reach 50.0%, where the correspondingly optimal work functions of the cathode and anode are 2.05 eV and 0.612 eV, respectively. The results presented in this work demonstrate that 3D DSs are superior to metallic materials and graphene in thermionic application, and will contribute to the choice of electrode materials, optimal designs, and control operative conditions of practical TIEGs.
Typology based on three density variables central to Spacematrix using cluster analysis

Since the publication of the book ‘Spacematrix. Space, density and urban form’ (Berghauser Pont and Haupt, 2010), the Spacematrix method has been linked back to its theoretical foundations by Steadman (2013), is further developed using the measure of accessible density to arrive at a density measure that more closely relates to the environment as experienced by people moving through the city (Berghauser Pont and Marcus, 2014) which then is used to arrive at a multi-scalar density typology (Berghauser Pont et al. 2017). This paper will take yet another step in the development of the Spacematrix method by including the measure of network density in the classification which until now was not used to its full potential. Important for successful classification is the ability to ascertain the fundamental characteristics on which the classification is to be based where the work of Berghauser Pont and Haupt (2010) will be followed addressing three key variables: Floor Space Index (FSI), Ground Space Index (GSI) and Network density (N) where especially the last was not fully included in the earlier work. Besides a typology based on these three variables, this paper will also result in a robust statistical method that can later be used on larger samples for city-scale comparisons. Two statistical methods are tested: hierarchical clustering and centroid-based clustering and besides a general discussion about their strong and weak points, the paper shows that the hierarchical method is more convincing in distinguishing differences in both building type and street pattern that is especially captured with Network density (N). As this method is not useful for large datasets we propose a combination of the two clustering methods as the way forward.
SACSoN: Scalable Autonomous Control for Social Navigation

Machine learning provides a powerful tool for building socially compliant robotic systems that go beyond simple predictive models of human behavior. By observing and understanding human interactions from past experiences, learning can enable effective social navigation behaviors directly from data. In this letter, our goal is to develop methods for training policies for socially unobtrusive behavior, such that robots can navigate among humans in ways that don't disturb human behavior in visual navigation using only onboard RGB observations. We introduce a definition for such behavior based on the counterfactual perturbation of the human: If the robot had not intruded into the space, would the human have acted in the same way? By minimizing this counterfactual perturbation, we can induce robots to behave in ways that do not alter the natural behavior of humans in the shared space. Instantiating this principle requires training policies to minimize their effect on human behavior, and this in turn requires data that allows us to model the behavior of humans in the presence of robots. Therefore, our approach is based on two key contributions. First, we collect a large dataset where an indoor mobile robot interacts with human bystanders. Second, we utilize this dataset to train policies that minimize counterfactual perturbation. We provide supplementary videos and make publicly available the visual navigation dataset on our project page.
Point Clouds Are Specialized Images: A Knowledge Transfer Approach for 3D Understanding

Self-supervised representation learning (SSRL) has gained increasing attention in point cloud understanding, in addressing the challenges posed by 3D data scarcity and high annotation costs. This paper presents PCExpert, a novel SSRL approach that reinterprets point clouds as"specialized images". This conceptual shift allows PCExpert to leverage knowledge derived from large-scale image modality in a more direct and deeper manner, via extensively sharing the parameters with a pre-trained image encoder in a multi-way Transformer architecture. The parameter sharing strategy, combined with a novel pretext task for pre-training, i.e., transformation estimation, empowers PCExpert to outperform the state of the arts in a variety of tasks, with a remarkable reduction in the number of trainable parameters. Notably, PCExpert's performance under LINEAR fine-tuning (e.g., yielding a 90.02% overall accuracy on ScanObjectNN) has already approached the results obtained with FULL model fine-tuning (92.66%), demonstrating its effective and robust representation capability.
Bursty Star Formation Naturally Explains the Abundance of Bright Galaxies at Cosmic Dawn

Recent discoveries of a significant population of bright galaxies at cosmic dawn z≳10 have enabled critical tests of cosmological galaxy formation models. In particular, the bright end of the galaxys’ UV luminosity functions (UVLFs) appear higher than predicted by many models. Using approximately 25,000 galaxy snapshots at 8 ≤ z ≤ 12 in a suite of FIRE-2 cosmological “zoom-in” simulations from the Feedback in Realistic Environments (FIRE) project, we show that the observed abundance of UV-bright galaxies at cosmic dawn is reproduced in these simulations with a multichannel implementation of standard stellar feedback processes, without any fine-tuning. Notably, we find no need to invoke previously suggested modifications, such as a nonstandard cosmology, a top-heavy stellar initial mass function, or a strongly enhanced star formation efficiency. We contrast the UVLFs predicted by bursty star formation in these original simulations to those derived from star formation histories (SFHs) smoothed over prescribed timescales (e.g., 100 Myr). The comparison demonstrates that the strongly time-variable SFHs predicted by the FIRE simulations play a key role in correctly reproducing the observed, bright-end UVLFs at cosmic dawn: the bursty SFHs induce order-or-magnitude changes in the abundance of UV-bright (M UV ≲ −20) galaxies at z ≳ 10. The predicted bright-end UVLFs are consistent with both the spectroscopically confirmed population and the photometrically selected candidates. We also find good agreement between the predicted and observationally inferred integrated UV luminosity densities, which evolve more weakly with redshift in FIRE than suggested by some other models.
Star Cluster Classification using Deep Transfer Learning with PHANGS-HST

Currently available star cluster catalogues from HST imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. The time-consuming nature of this process has limited the production of reliable catalogues and thus also post-observation analysis. To address this problem, deep transfer learning has recently been used to create neural network models which accurately classify star cluster morphologies at production scale for nearby spiral galaxies (D ≲ 20 Mpc). Here, we use HST UV-optical imaging of over 20,000 sources in 23 galaxies from the Physics at High Angular Resolution in Nearby GalaxieS (PHANGS) survey to train and evaluate two new sets of models: i) distance-dependent models, based on cluster candidates binned by galaxy distance (9–12 Mpc, 14–18 Mpc, 18–24 Mpc), and ii) distance-independent models, based on the combined sample of candidates from all galaxies. We find that the overall accuracy of both sets of models is comparable to previous automated star cluster classification studies (∼60–80%) and show improvement by a factor of two in classifying asymmetric and multi-peaked clusters from PHANGS-HST. Somewhat surprisingly, while we observe a weak negative correlation between model accuracy and galactic distance, we find that training separate models for the three distance bins does not significantly improve classification accuracy. We also evaluate model accuracy as a function of cluster properties such as brightness, colour, and SED-fit age. Based on the success of these experiments, our models will provide classifications for the full set of PHANGS-HST candidate clusters (N∼200,000) for public release.
Fast Dust Sand Image Enhancement Based on Color Correction and New Membership Function

Images captured in dusty environments suffering from poor visibility and quality. Enhancement of these images such as sand dust images plays a critical role in various atmospheric optics applications. In this work, proposed a new model based on Color Correction and new membership function to enhance san dust images. The proposed model consists of three phases: correction of color shift, removal of haze, and enhancement of contrast and brightness. The color shift is corrected using a new membership function to adjust the values of U and V in the YUV color space. The Adaptive Dark Channel Prior (A-DCP) is used for haze removal. The stretching contrast and improving image brightness are based on Contrast Limited Adaptive Histogram Equalization (CLAHE). The proposed model tests and evaluates through many real sand dust images. The experimental results show that the proposed solution is outperformed the current studies in terms of effectively removing the red and yellow cast and provides high quality and quantity dust images.
Prompt Guided Transformer for Multi-Task Dense Prediction

Task-conditional architecture offers advantage in parameter efficiency but falls short in performance compared to state-of-the-art multi-decoder methods. How to trade off performance and model parameters is an important and difficult problem. In this paper, we introduce a simple and lightweight task-conditional model called Prompt Guided Transformer (PGT) to optimize this challenge. Our approach designs a Prompt-conditioned Transformer block, which incorporates task-specific prompts in the self-attention mechanism to achieve global dependency modeling and parameter-efficient feature adaptation across multiple tasks. This block is integrated into both the shared encoder and decoder, enhancing the capture of intra- and inter-task features. Moreover, we design a lightweight decoder to further reduce parameter usage, which accounts for only 2.7% of the total model parameters. Extensive experiments on two multi-task dense prediction benchmarks, PASCAL-Context and NYUD-v2, demonstrate that our approach achieves state-of-the-art results among task-conditional methods while using fewer parameters, and maintains a significant balance between performance and parameter size.
Swiper and Dora: efficient solutions to weighted distributed problems

The majority of fault-tolerant distributed algorithms are designed assuming a nominal corruption model, in which at most a fraction $f_n$ of parties can be corrupted by the adversary. However, due to the infamous Sybil attack, nominal models are not sufficient to express the trust assumptions in open (i.e., permissionless) settings. Instead, permissionless systems typically operate in a weighted model, where each participant is associated with a weight and the adversary can corrupt a set of parties holding at most a fraction $f_w$ of total weight. In this paper, we suggest a simple way to transform a large class of protocols designed for the nominal model into the weighted model. To this end, we formalize and solve three novel optimization problems, which we collectively call the weight reduction problems, that allow us to map large real weights into small integer weights while preserving the properties necessary for the correctness of the protocols. In all cases, we manage to keep the sum of the integer weights to be at most linear in the number of parties, resulting in extremely efficient protocols for the weighted model. Moreover, we demonstrate that, on weight distributions that emerge in practice, the sum of the integer weights tends to be far from the theoretical worst-case and, often even smaller than the number of participants. While, for some protocols, our transformation requires an arbitrarily small reduction in resilience (i.e., $f_w = f_n - \epsilon$), surprisingly, for many important problems we manage to obtain weighted solutions with the same resilience ($f_w = f_n$) as nominal ones. Notable examples include asynchronous consensus, verifiable secret sharing, erasure-coded distributed storage and broadcast protocols.
Formation Control for Moving Target Enclosing via Relative Localization

In this paper, we investigate the problem of controlling multiple unmanned aerial vehicles (UAVs) to enclose a moving target in a distributed fashion based on a relative distance and self-displacement measurements. A relative localization technique is developed based on the recursive least square estimation (RLSE) technique with a forgetting factor to estimates both the “UAV-UAV” and “UAV-target” relative positions. The formation enclosing motion is planned using a coupled oscillator model, which generates desired motion for UAVs to distribute evenly on a circle. The coupled-oscillator-based motion can also facilitate the exponential convergence of relative localization due to its persistent excitation nature. Based on the generation strategy of desired formation pattern and relative localization estimates, a cooperative formation tracking control scheme is proposed, which enables the formation geometric center to asymptotically converge to the moving target. The asymptotic convergence performance is analyzed theoretically for both the relative localization technique and the formation control algorithm. Numerical simulations are provided to show the efficiency of the proposed algorithm. Experiments with three quadrotors tracking one target are conducted to evaluate the proposed target enclosing method in real platforms.
$\mathcal{N} = 2$ Schur index and line operators

4d $\mathcal{N} = 2$ SCFTs and their invariants can be often enriched by non-local BPS operators. In this paper we study the flavored Schur index of several types of N = 2 SCFTs with and without line operators, using a series of new integration formula of elliptic functions and Eisenstein series. We demonstrate how to evaluate analytically the Schur index for a series of $A_2$ class-$\mathcal{S}$ theories and the $\mathcal{N} = 4$ SO(7) theory. For all $A_1$ class-$\mathcal{S}$ theories we obtain closed-form expressions for SU(2) Wilson line index, and 't Hooft line index in some simple cases. We also observe the relation between the line operator index with the characters of the associated chiral algebras. Wilson line index for some other low rank gauge theories are also studied.
A study of the measurement of the $\tau$ lepton anomalous magnetic moment in high energy lead-lead collisions at LHC

The $\tau$ lepton anomalous magnetic moment: $a_\tau = \frac{g_{\tau}-2}{2}$ was measured, so far, with a precision of only several percents despite its highly sensitivity to physics beyond the Standard Model such as compositeness or Supersymmetry. A new study is presented to improve the sensitivity of the $a_\tau $ measurement with photon-photon interactions from ultra-peripheral lead-lead collisions at LHC. The theoretical approach used in this work is based on an effective Lagrangian and on a photon flux implemented in the MadGraph5 Monte Carlo simulation. Using a multivariate analysis to discriminate the signal from the background processes, a sensitivity to the anomalous magnetic moment $\rm{a_{\tau}}$ = 0 $_{+0.011} ^{-0.019}$ is obtained at 95\% CL with a dataset corresponding to an integrated luminosity of 2 nb$^{-1}$ of lead-lead collisions and assuming a conservative 10\% systematic uncertainty. The present results are compared with previous calculations and available measurements.
Teach Me How to Improve My Argumentation Skills: A Survey on Feedback in Argumentation

The use of argumentation in education has been shown to improve critical thinking skills for end-users such as students, and computational models for argumentation have been developed to assist in this process. Although these models are useful for evaluating the quality of an argument, they oftentimes cannot explain why a particular argument is considered poor or not, which makes it difficult to provide constructive feedback to users to strengthen their critical thinking skills. In this survey, we aim to explore the different dimensions of feedback (Richness, Visualization, Interactivity, and Personalization) provided by the current computational models for argumentation, and the possibility of enhancing the power of explanations of such models, ultimately helping learners improve their critical thinking skills.
Investigating the Learning Behaviour of In-context Learning: A Comparison with Supervised Learning

Large language models (LLMs) have shown remarkable capacity for in-context learning (ICL), where learning a new task from just a few training examples is done without being explicitly pre-trained. However, despite the success of LLMs, there has been little understanding of how ICL learns the knowledge from the given prompts. In this paper, to make progress toward understanding the learning behaviour of ICL, we train the same LLMs with the same demonstration examples via ICL and supervised learning (SL), respectively, and investigate their performance under label perturbations (i.e., noisy labels and label imbalance) on a range of classification tasks. First, via extensive experiments, we find that gold labels have significant impacts on the downstream in-context performance, especially for large language models; however, imbalanced labels matter little to ICL across all model sizes. Second, when comparing with SL, we show empirically that ICL is less sensitive to label perturbations than SL, and ICL gradually attains comparable performance to SL as the model size increases.
OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic Segmentation of Road Scenes

Light field cameras, by harnessing the power of micro-lens array, are capable of capturing intricate angular and spatial details. This allows for acquiring complex light patterns and details from multiple angles, significantly enhancing the precision of image semantic segmentation, a critical aspect of scene interpretation in vision intelligence. However, the extensive angular information of light field cameras contains a large amount of redundant data, which is overwhelming for the limited hardware resources of intelligent vehicles. Besides, inappropriate compression leads to information corruption and data loss. To excavate representative information, we propose a new paradigm, Omni-Aperture Fusion model (OAFuser), which leverages dense context from the central view and discovers the angular information from sub-aperture images to generate a semantically consistent result. To avoid feature loss during network propagation and simultaneously streamline the redundant information from the light field camera, we present a simple yet very effective Sub-Aperture Fusion Module (SAFM) to embed sub-aperture images into angular features without any additional memory cost. Furthermore, to address the mismatched spatial information across viewpoints, we present a Center Angular Rectification Module (CARM) to realize feature resorting and prevent feature occlusion caused by asymmetric information. Our proposed OAFuser achieves state-of-the-art performance on the UrbanLF-Real and -Syn datasets and sets a new record of 84.93% in mIoU on the UrbanLF-Real Extended dataset, with a gain of +4.53%. The source code of OAFuser will be available at https://github.com/FeiBryantkit/OAFuser.