Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Diverse and Consistent Typography Generation

In this work, we consider the typography generation task that aims at producing diverse typographic styling for the given graphic document. We formulate typography generation as a fine-grained attribute generation for multiple text elements and build an autoregressive model to generate diverse typography that matches the input design context. We further propose a simple yet effective sampling approach that respects the consistency and distinction principle of typography so that generated examples share consistent typographic styling across text elements. Our empirical study shows that our model successfully generates diverse typographic designs while preserving a consistent typographic structure.
Millimeter Wave Thin-Film Bulk Acoustic Resonator in Sputtered Scandium Aluminum Nitride

This work reports a millimeter wave (mmWave) thin-film bulk acoustic resonator (FBAR) in sputtered scandium aluminum nitride (ScAlN). This paper identifies challenges of frequency scaling sputtered ScAlN into mmWave and proposes a stack and new fabrication procedure with a sputtered Sc0.3 Al0.7 N on Al on Si carrier wafer. The resonator achieves electromechanical coupling (<inline-formula> <tex-math notation="LaTeX">${k} ^{2}$ </tex-math></inline-formula>) of 7.0% and quality factor (<inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula>) of 62 for the first-order symmetric (S1) mode at 21.4 GHz, along with <inline-formula> <tex-math notation="LaTeX">${k} ^{2}$ </tex-math></inline-formula> of 4.0% and <inline-formula> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> of 19 for the third-order symmetric (S3) mode at 55.4 GHz, showing higher figures of merit (FoM, <inline-formula> <tex-math notation="LaTeX">${k} ^{2} \cdot {Q}$ </tex-math></inline-formula>) than reported AlN/ScAlN-based mmWave acoustic resonators. The ScAlN quality is identified by transmission electron microscopy (TEM) and X-ray diffraction (XRD), identifying the bottlenecks in the existing piezoelectric-metal stack. Further improvement of ScAlN/AlN-based mmWave acoustic resonators calls for better crystalline quality from improved thin-film deposition methods. [2023-0151]
BEVTrack: A Simple and Strong Baseline for 3D Single Object Tracking in Bird's-Eye View

3D Single Object Tracking (SOT) is a fundamental task of computer vision, proving essential for applications like autonomous driving. It remains challenging to localize the target from surroundings due to appearance variations, distractors, and the high sparsity of point clouds. The spatial information indicating objects' spatial adjacency across consecutive frames is crucial for effective object tracking. However, existing trackers typically employ point-wise representation with irregular formats, leading to insufficient use of this important spatial knowledge. As a result, these trackers usually require elaborate designs and solving multiple subtasks. In this paper, we propose BEVTrack, a simple yet effective baseline that performs tracking in Bird's-Eye View (BEV). This representation greatly retains spatial information owing to its ordered structure and inherently encodes the implicit motion relations of the target as well as distractors. To achieve accurate regression for targets with diverse attributes (\textit{e.g.}, sizes and motion patterns), BEVTrack constructs the likelihood function with the learned underlying distributions adapted to different targets, rather than making a fixed Laplace or Gaussian assumption as in previous works. This provides valuable priors for tracking and thus further boosts performance. While only using a single regression loss with a plain convolutional architecture, BEVTrack achieves state-of-the-art performance on three large-scale datasets, KITTI, NuScenes, and Waymo Open Dataset while maintaining a high inference speed of about 200 FPS. The code will be released at https://github.com/xmm-prio/BEVTrack.
A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images

Self-supervised pretraining has been observed to be effective at improving feature representations for transfer learning, leveraging large amounts of unlabelled data. This review summarizes recent research into its usage in X-ray, computed tomography, magnetic resonance, and ultrasound imaging, concentrating on studies that compare self-supervised pretraining to fully supervised learning for diagnostic tasks such as classification and segmentation. The most pertinent finding is that self-supervised pretraining generally improves downstream task performance compared to full supervision, most prominently when unlabelled examples greatly outnumber labelled examples. Based on the aggregate evidence, recommendations are provided for practitioners considering using self-supervised learning. Motivated by limitations identified in current research, directions and practices for future study are suggested, such as integrating clinical knowledge with theoretically justified self-supervised learning methods, evaluating on public datasets, growing the modest body of evidence for ultrasound, and characterizing the impact of self-supervised pretraining on generalization.
Varying influences of the built environment on household travel in 15 diverse regions of the United States

This study pools household travel and built environment data from 15 diverse US regions to produce travel models with more external validity than any to date. It uses a large number of consistently defined built environmental variables to predict five household travel outcomes – car trips, walk trips, bike trips, transit trips and vehicle miles travelled (VMT). It employs multilevel modelling to account for the dependence of households in the same region on shared regional characteristics and estimates ‘hurdle’ models to account for the excess number of zero values in the distributions of dependent variables such as household transit trips. It tests built environment variables for three different buffer widths around household locations to see which scale best explains travel behaviour. The resulting models are appropriate for post-processing outputs of conventional travel demand models, and for sketch planning applications in traffic impact analysis, climate action planning and health impact assessment.
Global Solutions of the Compressible Euler-Poisson Equations for Plasma with Doping Profile for Large Initial Data of Spherical Symmetry

We establish the global-in-time existence of solutions of finite relative-energy for the multidimensional compressible Euler-Poisson equations for plasma with doping profile for large initial data of spherical symmetry. Both the total initial energy and the initial mass are allowed to be {\it unbounded}, and the doping profile is allowed to be of large variation. This is achieved by adapting a class of degenerate density-dependent viscosity terms, so that a rigorous proof of the inviscid limit of global weak solutions of the Navier-Stokes-Poisson equations with the density-dependent viscosity terms to the corresponding global solutions of the Euler-Poisson equations for plasma with doping profile can be established. New difficulties arise when tackling the non-zero varied doping profile, which have been overcome by establishing some novel estimates for the electric field terms so that the neutrality assumption on the initial data is avoided. In particular, we prove that no concentration is formed in the inviscid limit for the finite relative-energy solutions of the compressible Euler-Poisson equations with large doping profiles in plasma physics.
Sparse Function-space Representation of Neural Networks

Deep neural networks (NNs) are known to lack uncertainty estimates and struggle to incorporate new data. We present a method that mitigates these issues by converting NNs from weight space to function space, via a dual parameterization. Importantly, the dual parameterization enables us to formulate a sparse representation that captures information from the entire data set. This offers a compact and principled way of capturing uncertainty and enables us to incorporate new data without retraining whilst retaining predictive performance. We provide proof-of-concept demonstrations with the proposed approach for quantifying uncertainty in supervised learning on UCI benchmark tasks.
Estimation of Total Phenolic and Flavonoid Contents in Some Medicinal Plants and Their Antioxidant Activities

Quercetin was isolated from ethyl acetate fraction of a methanol extract of the outer scale of onion by repeated sephadex LH-20 column chromatography. Methanol, 50% aqueous methanol and 70% aqueous acetone extracts of different parts of four medicinal plants, Ficus benghalensis, Elaeocarpus sphaericus, Ipomea carnea and Azeratum conyzoides were prepared and screened for total phenolic and flavonoid contents and free radical scavenging activity. The total phenolics contents were measured spectrophotometrically by using Folin-Ciocalteu reagent and total flavonoids content by using aluminum chloride colorimetric method. Free radical scavenging activity was evaluated using 2,2-diphenyl-1-picryl-hydrazyl (DPPH) assay. All the investigated medicinal plant extracts contained high amount of phenolics. The highest amount was detected in 70% aqueous acetone extract of E. sphaericus (298.8±9.03 mg GAE/g extract) and lowest amount in 50% aqueous methanol extract of F. benghalensis (6.7±0.73 mg GAE/g extract). The highest amount of flavonoid was found in methanol extract (78.2±2.72 mg quercetin/g extract) and the lowest amount was detected in 50% aqueous methanol extract (2.1±0.25 mg quercetin/g extract) of F. benghalensis . DPPH free radical scavenging assay was carried out only in 70% acetone extracts. The highest IC50 value was observed for E. sphaericus (34.0 μg/ml). A strong linear correlation between total phenolic content and antioxidant activity was found indicating that the major antioxidants are phenolics. DOI: http://dx.doi. org/10.3126/njst.v15i1. 12010 Nepal Journal of Science and Technology Vol. 15, No.1 (2014) 53-60
Effective Multi-Graph Neural Networks for Illicit Account Detection on Cryptocurrency Transaction Networks

We study illicit account detection on transaction networks of cryptocurrencies that are increasi_testngly important in online financial markets. The surge of illicit activities on cryptocurrencies has resulted in billions of losses from normal users. Existing solutions either rely on tedious feature engineering to get handcrafted features, or are inadequate to fully utilize the rich semantics of cryptocurrency transaction data, and consequently, yield sub-optimal performance. In this paper, we formulate the illicit account detection problem as a classification task over directed multigraphs with edge attributes, and present DIAM, a novel multi-graph neural network model to effectively detect illicit accounts on large transaction networks. First, DIAM includes an Edge2Seq module that automatically learns effective node representations preserving intrinsic transaction patterns of parallel edges, by considering both edge attributes and directed edge sequence dependencies. Then utilizing the multigraph topology, DIAM employs a new Multigraph Discrepancy (MGD) module with a well-designed message passing mechanism to capture the discrepant features between normal and illicit nodes, supported by an attention mechanism. Assembling all techniques, DIAM is trained in an end-to-end manner. Extensive experiments, comparing against 14 existing solutions on 4 large cryptocurrency datasets of Bitcoin and Ethereum, demonstrate that DIAM consistently achieves the best performance to accurately detect illicit accounts, while being efficient. For instance, on a Bitcoin dataset with 20 million nodes and 203 million edges, DIAM achieves F1 score 96.55%, significantly higher than the F1 score 83.92% of the best competitor. The code is available at https://github.com/TommyDzh/DIAM.
Well-posedness and averaging principle for L\'evy-type McKean-Vlasov stochastic differential equations under local Lipschitz conditions

In this paper, we investigate a class of McKean-Vlasov stochastic differential equations under L\'evy-type perturbations. We first establish the existence and uniqueness theorem for solutions of the McKean-Vlasov stochastic differential equations by utilizing the Euler-like approximation. Then under some suitable conditions, we show that the solutions of McKean-Vlasov stochastic differential equations can be approximated by the solutions of the associated averaged McKean-Vlasov stochastic differential equations in the sense of mean square convergence. In contrast to the existing work, a novel feature is the use of a much weaker condition -- local Lipschitzian in the state variables, allowing for possibly super-linearly growing drift, but linearly growing diffusion and jump coefficients. Therefore, our results are suitable for a wider class of McKean-Vlasov stochastic differential equations.
The Coffee Farmers Revolt in Southern Mexico in the 1980s and 1990s

Theoretical framework structural features of Mexican coffee production historical context of the Sierra Juaraz coffee-growing region, 1920s-1970s from Inmecafe to neo-liberal reforms, 1973-96 the emergence of collective action in the Sierra Juarez, 1976-86 sustaining collective action in the Sierra Juarez, 1986-96.
Cost–utility analysis of commonly used anti-glaucoma interventions for mild-to-moderate primary open-angle glaucoma patients in rural and urban China

Objective An increasing number of studies have explored the clinical effects of antiglaucoma surgical procedures; however, economic evidence was scarce. We aimed to compare the cost-effectiveness between maximal medical treatment (MMT) and commonly used surgical procedures (trabeculectomy, Ahmed glaucoma valve implantation, gonioscopy-assisted transluminal trabeculotomy and ab interno canaloplasty). Design and setting A Markov model study. Participants A hypothetical cohort of 100 000 patients with mild-to-moderate primary open-angle glaucoma (POAG). Outcomes Data were obtained from public sources. The main outcomes were incremental cost–utility ratios (ICURs) using quality-adjusted life-years (QALYs). Sensitivity analyses were conducted to verify the robustness and sensitivity of base-case results. Main results Both cumulative costs and QALYs gained from surgical procedures (US$6045–US$13 598, 3.33–6.05 QALYs) were higher than those from MMT (US$3117–US$6458, 3.14–5.66 QALYs). Compared with MMT, all surgical procedures satisfied the cost-effectiveness threshold (lower than US$30 501 and US$41 568 per QALY gained in rural and urban settings, respectively). During the 5-year period, trabeculectomy produced the lowest ICUR (US$21 462 and US$15 242 per QALY gained in rural and urban settings, respectively). During the 10-year-follow-up, trabeculectomy still produced the lowest ICUR (US$13 379 per QALY gained) in urban setting; however, gonioscopy-assisted transluminal trabeculotomy (US$19 619 per QALY gained) and ab interno canaloplasty (US$18 003 per QALY gained) produced lower ICURs than trabeculectomy (US$19 675 per QALY gained) in rural areas. Base-case results were most sensitive to the utilities and costs of initial treatment and maintenance. Conclusions The long-term cost-effectiveness of commonly used surgical procedures could be better than the short-term cost-effectiveness for mild-to-moderate POAG patients in China. Health economic studies, supported by more rigorous structured real-world data, are needed to assess their everyday cost-effectiveness.
Global climate change and biota: evidence from foraminifera during the middle eocene climatic optimum

Paleonviromental and paleoceanographic reconstructions of the repercussions related to the middle eocene climatic optimum global warming event. The study has been focused on two mid-latitude locations (Alano section, NE Italy) and ODP Site 1263 (SE Atlantic) using micropaleontological (benthic foraminifera) and geochemical proxies.
Hmgb2 improves astrocyte to neuron conversion by increasing the chromatin accessibility of genes associated with neuronal maturation in a proneuronal factor-dependent manner

Background Direct conversion of reactive glial cells to neurons is a promising avenue for neuronal replacement therapies after brain injury or neurodegeneration. The overexpression of neurogenic fate determinants in glial cells results in conversion to neurons. For repair purposes, the conversion should ideally be induced in the pathology-induced neuroinflammatory environment. However, very little is known regarding the influence of the injury-induced neuroinflammatory environment and released growth factors on the direct conversion process. Results We established a new in vitro culture system of postnatal astrocytes without epidermal growth factor that reflects the direct conversion rate in the injured, neuroinflammatory environment in vivo. We demonstrated that the growth factor combination corresponding to the injured environment defines the ability of glia to be directly converted to neurons. Using this culture system, we showed that chromatin structural protein high mobility group box 2 (HMGB2) regulates the direct conversion rate downstream of the growth factor combination. We further demonstrated that Hmgb2 cooperates with neurogenic fate determinants, such as Neurog2, in opening chromatin at the loci of genes regulating neuronal maturation and synapse formation. Consequently, early chromatin rearrangements occur during direct fate conversion and are necessary for full fate conversion. Conclusions Our data demonstrate novel growth factor-controlled regulation of gene expression during direct fate conversion. This regulation is crucial for proper maturation of induced neurons and could be targeted to improve the repair process.
Promoting intestinal healing by exclusive enteral nutrition with TGF-β in a mouse model of colitis

Background and aims Exclusive Enteral Nutrition (EEN) is the first line of treatment for pediatric Crohn’s disease (CD), but its mechanisms of action remain poorly understood. We studied EEN nutritional composition and TGF-β effect in a mouse model of colitis, as well as the role of intestinal microbiota. Methods Mice were treated with Dextran Sulfate Sodium (DSS) during 5 days to induce colitis, until the inflammatory peak (day 7) or gut restitution (day 14). After DSS treatment, some of them received EEN formula such as Modulen IBD® (DM mice) or Infatrini Peptisorb® (DINF mice), with TGF-β supplementation or neutralization, and clinical inflammation was evaluated. After sacrifice, macroscopic and microscopic inflammation were analyzed, as well as intestinal permeability (IP). The composition of mucosal colonic microbiota was analyzed and fecal microbiota transplantation was performed to evaluate its capacity to mediate anti-inflammatory and pro-regenerative effect. Colonic crypts from DSS and EEN mice were cultured as 3D organoids and cellular properties were analyzed. Results DSS mice developed colitis, as evidenced by the weight loss and clinical inflammation. It was accompanied by macroscopic inflammation such as colon thickness and edemas, and an elevated IP. In contrast, EEN mice with TGF-β formula present faster weight recovery and decreased inflammatory parameters, with a normalized IP, suggesting gut restitution and functionality. These functional improvements were not obtained for EEN mice without TGF-β formula. Moreover, EEN with Modulen IBD® (DM mice) modified the microbiota in comparison to DSS condition and attenuated inflammation. In addition, the organoids from DM mice colonic crypts treated had an enhanced survival, and re-epithelialization capacity. Conclusions Both EEN formula have anti-inflammatory properties, certainly by the nutritional composition. However, TGF-β plays a significant role in intestinal restitution and restoring barrier function. These beneficial effects are partly mediated by the microbiota to maintain gut homeostasis.
How antisense transcripts can evolve to encode novel proteins

Protein coding features can emerge de novo in non coding transcripts, resulting in emer- gence of new protein coding genes. Studies across many species show that a large frac- tion large fraction of evolutionarily novel non-coding RNAs have an antisense overlap with protein coding genes. The open reading frames (ORFs) in these antisense RNAs could also overlap with existing ORFs. In this study, we investigate how the evolution an ORF could be constrained by its overlap with an existing ORF in three different read- ing frames. Using a combination of mathematical modeling and genome/transcriptome data analysis in two different model organisms, we show that antisense overlap can increase the likelihood of ORF emergence and reduce the likelihood of ORF loss, es- pecially in one of the three reading frames. In addition to rationalising the repeatedly reported prevalence of de novo emerged genes in antisense transcripts, our work also provides a generic modeling and an analytical framework that can be used to under- stand evolution of antisense genes.
NADPH oxidase 4-derived hydrogen peroxide counterbalances testosterone-induced endothelial dysfunction and migration

Background High levels of testosterone (Testo) are associated with cardiovascular risk by increasing reactive oxygen species (ROS) formation. NADPH oxidases (NOX) are the major source of ROS in the vasculature in cardiovascular diseases. NOX4 is a unique isotype, which produces hydrogen peroxide (H2O2), and its participation in cardiovascular biology is controversial. So far, it is unclear whether NOX4 protects from Testo-induced endothelial injury. Thus, we hypothesized that supraphysiological levels of Testo induce endothelial NOX4 expression to attenuate endothelial injury. Methods Human Mesenteric Vascular Endothelial Cells (HMEC) and Human Umbilical Vein Endothelial Cells (HUVEC) were treated with Testo (10−7 M) with or without a NOX4 inhibitor [GLX351322 (10-4 M)]. In vivo, 10-week-old C57Bl/6J male mice were treated with Testo (10 mg/kg) for 30 days to study endothelial function. Results Testo increased mRNA and protein levels of NOX4 in HMEC and HUVEC. Testo increased superoxide anion (O2−) and H2O2 production, which were abolished by NOX1 and NOX4 inhibition, respectively. Testo also attenuated bradykinin-induced NO production, which was further impaired by NOX4 inhibition. In vivo, Testo decreased H2O2 production in aortic segments and triggered endothelial dysfunction [decreased relaxation to acetylcholine (ACh)], which was further impaired by GLX351322 and by a superoxide dismutase and catalase mimetic (EUK134). Finally, Testo led to a dysregulated endothelial cells migration, which was exacerbated by GLX351322. Conclusion These data indicate that supraphysiological levels of Testo increase the endothelial expression and activity of NOX4 to counterbalance the deleterious effects caused by Testo in endothelial function.