Miracle-dz/UniFAR-FTUs huggingface dataset:
FPRF: Feed-Forward Photorealistic Style Transfer of Large-Scale 3D Neural Radiance Fields

We present FPRF, a feed-forward photorealistic style transfer method for large-scale 3D neural radiance fields. FPRF stylizes large-scale 3D scenes with arbitrary, multiple style reference images without additional optimization while preserving multi-view appearance consistency. Prior arts required tedious per-style/-scene optimization and were limited to small-scale 3D scenes. FPRF efficiently stylizes large-scale 3D scenes by introducing a style-decomposed 3D neural radiance field, which inherits AdaIN’s feed-forward stylization machinery, supporting arbitrary style reference images. Furthermore, FPRF supports multi-reference stylization with the semantic correspondence matching and local AdaIN, which adds diverse user control for 3D scene styles. FPRF also preserves multi-view consistency by applying semantic matching and style transfer processes directly onto queried features in 3D space. In experiments, we demonstrate that FPRF achieves favorable photorealistic quality 3D scene stylization for large-scale scenes with diverse reference images.
Relativistic modeling of cosmological structures with Bianchi IX spacetimes

We develop a relativistic framework to investigate the evolution of cosmological structures from the initial density perturbations to the highly non-linear regime. Our approach involves proposing a procedure to match 'best-fit', exact Bianchi IX (BIX) spacetimes to finite regions within the perturbed Friedmann-Lemaitre-Robertson-Walker universe characterized by a positive averaged spatial curvature. This method enables us to approximately track the non-linear evolution of the initial perturbation using an exact solution. Unlike standard perturbation theory and exact solutions with a high degree of symmetry (such as spherical symmetry), our approach is applicable to a generic initial data, with the only requirement being positive spatial curvature. By employing the BIX symmetries, we can systematically incorporate the approximate effects of shear and curvature into the process of collapse. Our approach addresses the limitations of both standard perturbation theory and highly symmetric exact solutions, providing valuable insights into the non-linear evolution of cosmological structures.
Kernelized Normalizing Constant Estimation: Bridging Bayesian Quadrature and Bayesian Optimization

In this paper, we study the problem of estimating the normalizing constant through queries to the black-box function f, which is the integration of the exponential function of f scaled by a problem parameter lambda. We assume f belongs to a reproducing kernel Hilbert space (RKHS), and show that to estimate the normalizing constant within a small relative error, the level of difficulty depends on the value of lambda: When lambda approaches zero, the problem is similar to Bayesian quadrature (BQ), while when lambda approaches infinity, the problem is similar to Bayesian optimization (BO). More generally, the problem varies between BQ and BO. We find that this pattern holds true even when the function evaluations are noisy, bringing new aspects to this topic. Our findings are supported by both algorithm-independent lower bounds and algorithmic upper bounds, as well as simulation studies conducted on a variety of benchmark functions.
LinguAlchemy: Fusing Typological and Geographical Elements for Unseen Language Generalization

Pretrained language models (PLMs) have shown remarkable generalization toward multiple tasks and languages. Nonetheless, the generalization of PLMs towards unseen languages is poor, resulting in significantly worse language performance, or even generating nonsensical responses that are comparable to a random baseline. This limitation has been a longstanding problem of PLMs raising the problem of diversity and equal access to language modeling technology. In this work, we solve this limitation by introducing LinguAlchemy, a regularization technique that incorporates various aspects of languages covering typological, geographical, and phylogenetic constraining the resulting representation of PLMs to better characterize the corresponding linguistics constraints. LinguAlchemy significantly improves the accuracy performance of mBERT and XLM-R on unseen languages by ~18% and ~2%, respectively compared to fully finetuned models and displaying a high degree of unseen language generalization. We further introduce AlchemyScale and AlchemyTune, extension of LinguAlchemy which adjusts the linguistic regularization weights automatically, alleviating the need for hyperparameter search. LinguAlchemy enables better cross-lingual generalization to unseen languages which is vital for better inclusivity and accessibility of PLMs.
Optimistic Model Rollouts for Pessimistic Offline Policy Optimization

Model-based offline reinforcement learning (RL) has made remarkable progress, offering a promising avenue for improving generalization with synthetic model rollouts. Existing works primarily focus on incorporating pessimism for policy optimization, usually via constructing a Pessimistic Markov Decision Process (P-MDP). However, the P-MDP discourages the policies from learning in out-of-distribution (OOD) regions beyond the support of offline datasets, which can under-utilize the generalization ability of dynamics models. In contrast, we propose constructing an Optimistic MDP (O-MDP). We initially observed the potential benefits of optimism brought by encouraging more OOD rollouts. Motivated by this observation, we present ORPO, a simple yet effective model-based offline RL framework. ORPO generates Optimistic model Rollouts for Pessimistic offline policy Optimization. Specifically, we train an optimistic rollout policy in the O-MDP to sample more OOD model rollouts. Then we relabel the sampled state-action pairs with penalized rewards, and optimize the output policy in the P-MDP. Theoretically, we demonstrate that the performance of policies trained with ORPO can be lower-bounded in linear MDPs. Experimental results show that our framework significantly outperforms P-MDP baselines by a margin of 30%, achieving state-of-the-art performance on the widely-used benchmark. Moreover, ORPO exhibits notable advantages in problems that require generalization.
First-principles calculations of the electronic and optical properties of penta-graphene monolayer: study of many-body effects

In the present work, first-principles calculations based on the density functional theory (DFT), GW approximation and Bethe-Salpeter equation (BSE) are performed to study the electronic and optical properties of penta-graphene (PG) monolayer. The results indicated that PG is a semiconductor with an indirect band gap of approximately 2.32 eV at the DFT- GGA level. We found that the utilization of the GW approximation based on many-body perturbation theory led to an increase in the band gap, resulting in a quasi-direct gap of 5.35 eV. Additionally, we employed the G0W0 - RP A and G0W0 - BSE approximations to calculate the optical spectra in the absence and in the presence of electron-hole interaction, respectively. The results demonstrated that the inclusion of electron-hole interaction caused a red-shift of the absorption spectrum towards lower energies compared to the spectrum obtained from the G0W0 - RP A approximation. With the electron-hole interaction, it is found that the optical absorption spectra are dominated by the first bound exciton with a significant binding energy 3.07 eV. The study concluded that the PG monolayer, with a wider band gap and enhanced excitonic effects, holds promise as a suitable candidate for the design and fabrication of optoelectronic components.
Reciprocal Analysis of Sensible and Latent Heat Fluxes in a Forest Region Using Single Height Temperature and Humidity Based on the Bowen Ratio Concept

Evapotranspiration in forests has been researched for a long time because it serves an important role in water resource issues and biomass production. By applying the reciprocal analysis based on the Bowen ratio concept to the canopy surface, the sum result of sensible and latent heat fluxes, i.e., actual evapotranspiration (ET), is estimated from engineering aspect using the net radiation (Rn) and heat flux into the ground (G). The new method uses air temperature and humidity at a single height by determining the relative humidity (rehs) using the canopy temperature (Ts). The validity of the method is confirmed by the latent heat flux (lE) and sensible heat flux (H) observed by mean of eddy covariance method. The heat imbalance is corrected by multiple regression analysis. The temporal change of lE and H at the canopy surface is clarified using hourly and yearly data. Furthermore, the observed and estimated monthly evapotranspiration of the sites are compared. The research is conducted using hourly data and the validation of the method is conducted using observed covariance at five sites in the world using FLUXNET.
Acute Invasive Pulmonary Aspergillosis: Clinical Presentation and Treatment

Abstract Among all clinical manifestations of pulmonary aspergillosis, invasive pulmonary aspergillosis (IPA) is the most acute presentation. IPA is caused by Aspergillus hyphae invading the pulmonary tissue, causing either tracheobronchitis and/or bronchopneumonia. The degree of fungal invasion into the respiratory tissue can be seen as a spectrum, going from colonization to deep tissue penetration with angio-invasion, and largely depends on the host's immune status. Patients with prolonged, severe neutropenia and patients with graft-versus-host disease are at particularly high risk. However, IPA also occurs in other groups of immunocompromised and nonimmunocompromised patients, like solid organ transplant recipients or critically ill patients with severe viral disease. While a diagnosis of proven IPA is challenging and often warranted by safety and feasibility, physicians must rely on a combination of clinical, radiological, and mycological features to assess the likelihood for the presence of IPA. Triazoles are the first-choice regimen, and the choice of the drug should be made on an individual basis. Adjunctive therapy such as immunomodulatory treatment should also be taken into account. Despite an improving and evolving diagnostic and therapeutic armamentarium, the burden and mortality of IPA still remains high. This review aims to give a comprehensive and didactic overview of the current knowledge and best practices regarding the epidemiology, clinical presentation, diagnosis, and treatment of acute IPA.
Fissured tongue: A cross-sectional study

Background: Fissured tongue (lingua plicata) is a mostly asymptomatic condition characterized by grooves and fissures of varying depth on the dorsal surface of the tongue. Most reports in the literature indicate a prevalence of 10–20%, although there is marked variation Objectives: The objectives of the study are: 1. To assess the most prevalent pattern of fissured tongue in patients 2. To assess the possible association between the occurrence of fissured tongue with age, gender, symptoms and medical illness. Methods: A cross sectional study was conducted in our department of oral medicine and radiology. Fissured tongue was diagnosed clinically based on the presence of grooves on the dorsal and lateral aspects of the tongue and also the pattern of fissure. The subjects were asked about their habit history, symptoms related to tongue lesions and medical history. Statistical analysis: Chi square test was done to assess the relation between fissured tongue with age, sex, habits, symptoms and medical illness. Results: Out of 100 subjects screened, 68 were males and 32 were females. The fissures were found to be least in the 0-20 year age group, 4 (4 %) and it was most prevalent in the 21-40 age group, 38 (38 %), followed by 4160, 33 (33 %) and 61-80, 25 (25 %) years of age. The most prevalent pattern of fissure was found to be central longitudinal fissuring, 50 (50 %), followed by transverse fissures arising from a central fissure 16 (16 %), then type I, 13 (13 %) type V, 13 (13 %), type VI, 6 (6 %) and type III, 2(2 %). The least prevalent pattern was type III, double fissures. There was no significant association of fissured tongue with systemic conditions. Conclusion: The fissured tongue was most prevalent in the 21-40 age group and in the males. Central longitudinal fissuring was the most prevalent pattern seen. The occurrence of fissured tongue showed no association with any systemic conditions.
Metatranscriptomic analysis reveals synergistic activities of comammox and anammox bacteria in full-scale attached growth nitrogen removal system

Leveraging comammox Nitrospira and anammox bacteria for shortcut nitrogen removal can drastically lower the carbon footprint of wastewater treatment facilities by decreasing aeration energy, carbon, alkalinity, and tank volume requirements while also potentially reducing nitrous oxide emissions. However, their co-occurrence as dominant nitrifying bacteria is rarely reported in full-scale wastewater treatment. As a result, there is poor understanding of how operational parameters, in particular dissolved oxygen, impact their activity and synergistic behavior. Here, we report the impact of dissolved oxygen concentration (DO = 2, 4, 6 mg/L) on the microbial community’s transcriptomic expression in a full-scale integrated fixed film activated sludge (IFAS) municipal wastewater treatment facility predominantly performed by comammox Nitrospira and anammox bacterial populations. 16S rRNA transcript compositions revealed anammox bacteria and Nitrospira were significantly more active in IFAS biofilms compared to suspended sludge biomass. In IFAS biofilms, anammox bacteria significantly increased hzo expression at lower dissolved oxygen concentrations and this increase was highly correlated with the amoA expression levels of comammox bacteria. Interestingly, the genes involved in nitrite oxidation by comammox bacteria were significantly more upregulated relative to the genes involved in ammonia oxidation with decreasing dissolved oxygen concentrations. Ultimately, our findings suggest that comammox Nitrospira supply anammox bacteria with nitrite via ammonia oxidation and that this synergistic behavior is dependent on dissolved oxygen concentrations. Synopsis Comammox bacteria differentially regulate ammonia and nitrite oxidation in response to dissolved oxygen concentration suggesting dissolved oxygen dependence of their synergistic nitrogen removal with anammox bacteria in IFAS biofilms.
Genetic parameters for Icelandic dairy cows using a random regression test-day model

A total of 480,495 test-day yield records of 33,052 cows were used to estimate the genetic parameters for daily milk yield (MY), fat yield (FY), protein yield (PY) and somatic cell score (SCS) of Icelandic dairy cows in the first three lactations with a random regression model. Heritability of all traits was lowest in early lactation in all lactations and highest in midor late lactation. Heritability of lactation yields for the first lactation was 0.43, 0.39 and 0.41 for MY, FY and PY, respectively, but was estimated as lower when using a lactation model. Heritability of SCS in the first lactation was 0.23 using the random regression model but 0.15 using the lactation model. Heritability of persistency of lactation MY, FY and PY were 0.14-0.24 in all lactations and genetic correlations to the whole lactation SCS were -0.08 to -0.13. Heritability of yields had increased from previous estimates for the breed. Genetic variation of persistency in the population makes change of the lactation curve possible through selection.
Definition of regulatory elements and transcription factors controlling immune cell gene expression at single cell resolution using single nucleus ATAC-seq

The transcriptome of porcine peripheral blood mononuclear cells (PBMC) at single cell (sc) resolution is well described, but little is understood about the cis-regulatory mechanism behind scPBMC gene expression. Here, we profiled the open chromatin landscape of porcine PBMC using single nucleus ATAC sequencing (snATAC-seq). Approximately 22% of the identified peaks overlapped with annotated transcription start sites (TSS). Using clustering based on open chromatin pattern similarity, we demonstrate that cell type annotations using snATAC-seq are highly concordant to that reported for sc RNA sequencing (scRNA-seq). The differentially accessible peaks (DAPs) for each cell type were characterized and the pattern of accessibility of the DAPs near cell type markers across cell types was similar to that of the average gene expression level of corresponding marker genes. Additionally, we found that peaks identified in snATAC-seq have the potential power to predict the cell type specific transcription starting site (TSS). We identified both transcription factors (TFs) whose binding motif were enriched in cell type DAPs of multiple cell types and cell type specific TFs by conducting transcription factor binding motif (TFBM) analysis. Furthermore, we identified the putative enhancer or promoter regions bound by TFs for each differentially expressed gene (DEG) having a DAP that overlapped with its TSS by generating cis-co-accessibility networks (CCAN). To predict the regulators of such DEGs, TFBM analysis was performed for each CCAN. The regulator TF-target DEG pair predicted in this way was largely consistent with the results reported in the ENCODE Transcription Factor Targets Dataset (TFTD). This snATAC-seq approach provides insights into the chromatin accessibility landscape of porcine PBMCs and enables discovery of TFs predicted to control DEG through binding regulatory elements whose chromatin accessibility correlates with the DEG promoter region.
Machine Learning Uncovers the Transcriptional Regulatory Network for the Production Host Streptomyces albidoflavus J1074

Streptomyces albidoflavus J1074 is a popular and genetically tractable platform strain used for drug discovery and production via the expression of heterologous biosynthetic gene clusters (BGCs). However, its transcriptional regulation network (TRN) and its impact on secondary metabolism are poorly understood. Here we characterized its TRN by applying an independent component analysis to a compendium of 142 high quality RNA-Seq transcriptomes, from both in-house and public sources spanning 58 unique growth conditions. We obtained 63 independently modulated sets of genes (iModulons), that quantitatively describe the TRN and its activity state across diverse conditions. Through analyses of condition-dependent TRN activity states, we (i) describe how the TRN adapts to different growth conditions, (ii) conduct a cross-species iModulon comparison, uncovering shared features and unique characteristics of the TRN across lineages, (iii) identify putative regulators of several biosynthetic gene clusters, including Surugamide, Cyclofaulknamycin and Dudomycin, and (iv) infer potential functions of 40% of the uncharacterized genes in the S. albidoflavus J1074 genome. Our findings provide a comprehensive and quantitative understanding of the TRN of S. albidoflavus J1074, and suggest new strategies for rational strain design to optimize its production capabilities.
Microbial communities associated with marine sponges from diverse geographic locations harbour biosynthetic novelty

Marine sponges are a prolific source of biologically active small molecules, many of which originate from sponge-associated microbes. Identifying the producing microbes is a key challenge in developing sustainable routes for production and isolation of sponge-associated metabolites, and requires application of several computational tools. To facilitate these analyses, we developed MetaSing, a reproducible singularity-based pipeline for assembly, identification of high quality metagenome-assembled genomes (MAGs), and analysis biosynthetic gene clusters (BGCs) from metagenomic short read data. We apply this pipeline to metagenome datasets from 16 marine sponges collected from New Zealand, Tonga and the Mediterranean Sea. Our analysis yielded 643 MAGs representing 510 species. Of the 2,670 BGCs identified across all samples, 70.8% were linked to a MAG, enabling taxonomic characterisation. Further comparison of BGCs to those identified from previously sequenced microbes revealed high biosynthetic novelty in variety of underexplored phyla including Poribacteria, Acidobacteriota and Dadabacteria. Alongside the observation that each sample contains unique biosynthetic potential, this holds great promise for natural product discovery and for furthering the understanding of different sponge holobionts.
Using machine learning techniques to investigate learner engagement with TikTok media literacy campaigns

Abstract Social media has the unique capacity to expose many learners to media literacy instruction via targeted campaigns. Investigating learner engagement and reaction to these efforts may be a fruitful endeavor for researchers that can inform the design of future campaigns. However, the massive datasets associated with social media posts are difficult, and often impossible, to analyze with traditional qualitative methods. This study seeks to address this problem by leveraging machine learning techniques to collect and analyze Big Data from two different media literacy campaigns on the youth-oriented social media platform TikTok. Specifically, we explore the ways topic modeling, sentiment analysis, and network analysis can provide insight into learner engagement with these campaigns and discuss limitations and implications for stakeholders interested in utilizing these approaches.
Security and Privacy Issues in Deep Reinforcement Learning: Threats and Countermeasures

Deep Reinforcement Learning (DRL) is an essential subfield of Artificial Intelligence (AI), where agents interact with environments to learn policies for solving complex tasks. In recent years, DRL has achieved remarkable breakthroughs in various tasks, including video games, robotic control, quantitative trading, and autonomous driving. Despite its accomplishments, security and privacy-related issues still prevent us from deploying trustworthy DRL applications. For example, by manipulating the environment, an attacker can influence an agent’s actions, misleading it to behave abnormally. Additionally, an attacker can infer private training data and environmental information by maliciously interacting with DRL models, causing a privacy breach. In this survey, we systematically investigate the recent progress of security and privacy issues in the context of DRL. First, we present a holistic review of security-related attacks within DRL systems from the perspectives of single-agent and multi-agent systems, and review privacy-related attacks. Second, we review and classify defense methods used to address security-related challenges, including robust learning, anomaly detection, and game theory approaches. Third, we review and classify privacy-preserving technologies, including encryption, differential privacy, and policy confusion. Finally, we conclude the survey by discussing open issues and possible directions for future research in this field.