Miracle-dz/UniFAR-FTUs huggingface dataset:
Atmospheric Convection Plays a Key Role in the Climate of Tidally Locked Terrestrial Exoplanets: Insights from High-resolution Simulations

Using a 3D general circulation model (GCM), we investigate the sensitivity of the climate of tidally locked Earthlike exoplanets, Trappist-1e and Proxima Centauri b, to the choice of a convection parameterization. Compared to a mass-flux convection parameterization, a simplified convection adjustment parameterization leads to a >60% decrease of the cloud albedo, increasing the mean dayside temperature by . The representation of convection also affects the atmospheric conditions of the night side, via a change in planetary-scale wave patterns. As a result, using the convection adjustment scheme makes the nightside cold traps warmer by 17–36 K for the planets in our simulations. The day–night thermal contrast is sensitive to the representation of convection in 3D GCM simulations, so caution should be taken when interpreting emission phase curves. The choice of convection treatment, however, does not alter the simulated climate enough to result in a departure from habitable conditions, at least for the atmospheric composition and planetary parameters used in our study. The near-surface conditions both in the Trappist-1e and Proxima b cases remain temperate, allowing for an active water cycle. We further advance our analysis using high-resolution model experiments, in which atmospheric convection is simulated explicitly. Our results suggest that in a hypothetical global convection-permitting simulation, the surface temperature contrast would be higher than in the coarse-resolution simulations with parameterized convection. In other words, models with parameterized convection may overestimate the inter-hemispheric heat redistribution efficiency.
Streaming Networks: Increase Noise Robustness and Filter Diversity via Hard-wired and Input-induced Sparsity

The CNNs have achieved a state-of-the-art performance in many applications. Recent studies illustrate that CNN's recognition accuracy drops drastically if images are noise corrupted. We focus on the problem of robust recognition accuracy of noise-corrupted images. We introduce a novel network architecture called Streaming Networks. Each stream is taking a certain intensity slice of the original image as an input, and stream parameters are trained independently. We use network capacity, hard-wired and input-induced sparsity as the dimensions for experiments. The results indicate that only the presence of both hard-wired and input-induces sparsity enables robust noisy image recognition. Streaming Nets is the only architecture which has both types of sparsity and exhibits higher robustness to noise. Finally, to illustrate increase in filter diversity we illustrate that a distribution of filter weights of the first conv layer gradually approaches uniform distribution as the degree of hard-wired and domain-induced sparsity and capacities increases.
$\beta$-High Resolution ODE and Phase Transition between NAG-SC and Heavy Ball Method

In this paper, we study the convergence properties of an algorithm that can be viewed as an interpolation between two gradient based optimization methods, Nesterov's acceleration method for strongly convex functions $(NAG$-$SC)$ and Polyak's heavy ball method. Recent Progress has been made on using High-Resolution ordinary differential equations (ODEs) to distinguish these two fundamentally different methods. The key difference between them can be attributed to the gradient correction term, which is reflected by the Hessian term in the High-Resolution ODE. Our goal is to understand how this term can affect the convergence rate and the choice of our step size. To achieve this goal, we introduce the notion of $\beta$-High Resolution ODE, $0\leq \beta\leq 1$ and prove that within certain range of step size, there is a phase transition happening at $\beta_c$. When $\beta_c\leq\beta\leq 1$, the algorithm associated with $\beta$-High Resolution ODE have the same convergence rate as NAG-SC. When $0\leq \beta\leq \beta_c$, this algorithm will have the slower convergence rate than NAG-SC.
LUVLi Face Alignment: Estimating Landmarks’ Location, Uncertainty, and Visibility Likelihood

Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted locations nor predict whether landmarks are visible. In this paper, we present a novel framework for jointly predicting landmark locations, associated uncertainties of these predicted locations, and landmark visibilities. We model these as mixed random variables and estimate them using a deep network trained using our proposed Location, Uncertainty, and Visibility Likelihood (LUVLi) loss. In addition, we release an entirely new labeling of a large face alignment dataset with over 19,000 face images in a full range of head poses. Each face is manually labeled with the ground-truth locations of 68 landmarks, with the additional information of whether each landmarks is visible, self-occluded (due to extreme head poses), or externally occluded. Not only does our joint estimation yield accurate estimates of the uncertainty of predicted landmark locations, but it also yields state-of-the-art estimates for the landmark locations themselves on mulitple standard face alignment datasets. Our method's estimates of the uncertainty of predicted landmark locations could be used to automatically identify input images on which face alignment fails, which can be critical for downstream tasks.
Goeritz Groups of Bridge Decompositions

For a bridge decomposition of a link in the $3$-sphere, we define the Goeritz group to be the group of isotopy classes of orientation-preserving homeomorphisms of the $3$-sphere that preserve each of the bridge sphere and link setwise. After describing basic properties of this group, we discuss the asymptotic behavior of the minimal pseudo-Anosov entropies. We then give an application to the asymptotic behavior of the minimal entropies for the original Goeritz groups of Heegaard splittings of the $3$-sphere and the real projective space.
Parametrization of Neural Networks with Connected Abelian Lie Groups as Data Manifold

Neural nets have been used in an elusive number of scientific disciplines. Nevertheless, their parameterization is largely unexplored. Dense nets are the coordinate transformations of a manifold from which the data is sampled. After processing through a layer, the representation of the original manifold may change. This is crucial for the preservation of its topological structure and should therefore be parameterized correctly. We discuss a method to determine the smallest topology preserving layer considering the data domain as abelian connected Lie group and observe that it is decomposable into $\mathbb{R}^p \times \mathbb {T}^q$. Persistent homology allows us to count its $k$-th homology groups. Using K\"unneth's theorem, we count the $k$-th Betti numbers. Since we know the embedding dimension of $\mathbb{R}^p$ and $\mathcal{S}^1$, we parameterize the bottleneck layer with the smallest possible matrix group, which can represent a manifold with those homology groups. Resnets guarantee smaller embeddings due to the dimension of their state space representation.
RSS-Net: Weakly-Supervised Multi-Class Semantic Segmentation with FMCW Radar

This paper presents an efficient annotation procedure and an application thereof to end-to-end, rich semantic segmentation of the sensed environment using Frequency-Modulated Continuous-Wave scanning radar. We advocate radar over the traditional sensors used for this task as it operates at longer ranges and is substantially more robust to adverse weather and illumination conditions. We avoid laborious manual labelling by exploiting the largest radar-focused urban autonomy dataset collected to date, correlating radar scans with RGB cameras and LiDAR sensors, for which semantic segmentation is an already consolidated procedure. The training procedure leverages a state-of-the-art natural image segmentation system which is publicly available and as such, in contrast to previous approaches, allows for the production of copious labels for the radar stream by incorporating four camera and two LiDAR streams. Additionally, the losses are computed taking into account labels to the radar sensor horizon by accumulating LiDAR returns along a pose-chain ahead and behind of the current vehicle position. Finally, we present the network with multi-channel radar scan inputs in order to deal with ephemeral and dynamic scene objects.
Inexpensive Domain Adaptation of Pretrained Language Models: A Case Study on Biomedical Named Entity Recognition

Domain adaptation of Pretrained Language Models (PTLMs) is typically achieved by pretraining on in-domain text. While successful, this approach is expensive in terms of hardware, runtime and CO_2 emissions. Here, we propose a cheaper alternative: We train Word2Vec on in-domain text and align the resulting word vectors with the input space of a general-domain PTLM (here: BERT). We evaluate on eight biomedical Named Entity Recognition (NER) tasks and compare against the recently proposed BioBERT model (Lee et al., 2020). We cover over 50% of the BioBERT-BERT F1 delta, at 5% of BioBERT's CO_2 footprint and 2% of its cloud compute cost.
Forecasting in multivariate irregularly sampled time series with missing values

Sparse and irregularly sampled multivariate time series are common in clinical, climate, financial and many other domains. Most recent approaches focus on classification, regression or forecasting tasks on such data. In forecasting, it is necessary to not only forecast the right value but also to forecast when that value will occur in the irregular time series. In this work, we present an approach to forecast not only the values but also the time at which they are expected to occur.
Multiform Fonts-to-Fonts Translation via Style and Content Disentangled Representations of Chinese Character

This paper mainly discusses the generation of personalized fonts as the problem of image style transfer. The main purpose of this paper is to design a network framework that can extract and recombine the content and style of the characters. These attempts can be used to synthesize the entire set of fonts with only a small amount of characters. The paper combines various depth networks such as Convolutional Neural Network, Multi-layer Perceptron and Residual Network to find the optimal model to extract the features of the fonts character. The result shows that those characters we have generated is very close to real characters, using Structural Similarity index and Peak Signal-to-Noise Ratio evaluation criterions.
Large Induced Matchings in Random Graphs

Given a large graph $H$, does the binomial random graph $G(n,p)$ contain a copy of $H$ as an induced subgraph with high probability? This classical question has been studied extensively for various graphs $H$, going back to the study of the independence number of $G(n,p)$ by Erdős and Bollobas, and Matula in 1976. In this paper we prove an asymptotically best possible result for induced matchings by showing that if $C/n\le p \le 0.99$ for some large constant $C$, then $G(n,p)$ contains an induced matching of order approximately $2\log_q(np)$, where $q= \frac{1}{1-p}$.
Robust Empirical Bayes Confidence Intervals

We construct robust empirical Bayes confidence intervals (EBCIs) in a normal means problem. The intervals are centered at the usual linear empirical Bayes estimator, but use a critical value accounting for shrinkage. Parametric EBCIs that assume a normal distribution for the means (Morris (1983b)) may substantially undercover when this assumption is violated. In contrast, our EBCIs control coverage regardless of the means distribution, while remaining close in length to the parametric EBCIs when the means are indeed Gaussian. If the means are treated as fixed, our EBCIs have an average coverage guarantee: the coverage probability is at least 1 − α on average across the n EBCIs for each of the means. Our empirical application considers the effects of U.S. neighborhoods on intergenerational mobility.
Low-temperature synthesis and electrocatalytic application of large-area PtTe2 thin films

The synthesis of transition metal dichalcogenides (TMDs) has been a primary focus for 2D nanomaterial research over the last 10 years, however, only a small fraction of this research has been concentrated on transition metal ditellurides. In particular, nanoscale platinum ditelluride (PtTe2) has rarely been investigated, despite its potential applications in catalysis, photonics and spintronics. Of the reports published, the majority examine mechanically-exfoliated flakes from chemical vapor transport (CVT) grown crystals. This method produces high quality-crystals, ideal for fundamental studies. However, it is very resource intensive and difficult to scale up meaning there are significant obstacles to implementation in large-scale applications. In this report, the synthesis of thin films of PtTe2 through the reaction of solid-phase precursor films is described. This offers a production method for large-area, thickness-controlled PtTe2, potentially suitable for a number of applications. These polycrystalline PtTe2 films were grown at temperatures as low as 450 °C, significantly below the typical temperatures used in the CVT synthesis methods. Adjusting the growth parameters allowed the surface coverage and morphology of the films to be controlled. Analysis with scanning electron- and scanning tunneling microscopy indicated grain sizes of above 1 µm could be achieved, comparing favorably with typical values of ∼50 nm for polycrystalline films. To investigate their potential applicability, these films were examined as electrocatalysts for the hydrogen evolution reaction (HER) and oxygen reduction reaction (ORR). The films showed promising catalytic behavior, however, the PtTe2 was found to undergo chemical transformation to a substoichiometric chalcogenide compound under ORR conditions. This study shows while PtTe2 is stable and highly useful for in HER, this property does not apply to ORR, which undergoes a fundamentally different mechanism. This study broadens our knowledge on the electrocatalysis of TMDs.
Data Driven Tight Frame for Compressed Sensing MRI Reconstruction via Off-the-Grid Regularization

Recently, the finite-rate-of-innovation (FRI) based continuous domain regularization is emerging as an alternative to the conventional on-the-grid sparse regularization for the compressed sensing (CS) due to its ability to alleviate the basis mismatch between the true support of the shape in the continuous domain and the discrete grid. In this paper, we propose a new off-the-grid regularization for the CS-MRI reconstruction. Following the recent works on two dimensional FRI, we assume that the discontinuities/edges of the image are localized in the zero level set of a band-limited periodic function. This assumption induces the linear dependencies among the Fourier samples of the gradient of the image, which leads to a low rank two-fold Hankel matrix. We further observe that the singular value decomposition of a low rank Hankel matrix corresponds to an adaptive tight frame system which can represent the image with sparse canonical coefficients. Based on this observation, we propose a data driven tight frame based off-the-grid regularization model for the CS-MRI reconstruction. To solve the nonconvex and nonsmooth model, a proximal alternating minimization algorithm with a guaranteed global convergence is adopted. Finally, the numerical experiments show that our proposed data driven tight frame based approach outperforms the existing approaches.
Deep Neural Network Learning with Second-Order Optimizers - a Practical Study with a Stochastic Quasi-Gauss-Newton Method

Training in supervised deep learning is computationally demanding, and the convergence behavior is usually not fully understood. We introduce and study a second-order stochastic quasi-Gauss-Newton (SQGN) optimization method that combines ideas from stochastic quasi-Newton methods, Gauss-Newton methods, and variance reduction to address this problem. SQGN provides excellent accuracy without the need for experimenting with many hyper-parameter configurations, which is often computationally prohibitive given the number of combinations and the cost of each training process. We discuss the implementation of SQGN with TensorFlow, and we compare its convergence and computational performance to selected first-order methods using the MNIST benchmark and a large-scale seismic tomography application from Earth science.
SC4D: A Sparse 4D Convolutional Network for Skeleton-Based Action Recognition

In this paper, a new perspective is presented for skeleton-based action recognition. Specifically, we regard the skeletal sequence as a spatial-temporal point cloud and voxelize it into a 4-dimensional grid. A novel sparse 4D convolutional network (SC4D) is proposed to directly process the generated 4D grid for high-level perceptions. Without manually designing the hand-crafted transformation rules, it makes better use of the advantages of the convolutional network, resulting in a more concise, general and robust framework for skeletal data. Besides, by processing the space and time simultaneously, it largely keeps the spatial-temporal consistency of the skeletal data, and thus brings better expressiveness. Moreover, with the help of the sparse tensor, it can be efficiently executed with less computations. To verify the superiority of SC4D, extensive experiments are conducted on two challenging datasets, namely, NTU-RGBD and SHREC, where SC4D achieves state-of-the-art performance on both of them.
Transmitted human immunodeficiency virus-1 drug resistance in a cohort of men who have sex with men in Belo Horizonte, Brazil - 1996-2012

The presence of transmitted human immunodeficiency virus (HIV)-1 drug-resistance (TDR) at the time of antiretroviral therapy initiation is associated with failure to achieve viral load (VL) suppression. Here, we report TDR surveillance in a specific population of men who have sex with men (MSM) in Belo Horizonte, Brazil. In this study, the rate of TDR was evaluated in 64 HIV-infected individuals from a cohort of MSM between 1996-June 2012. Fifty-four percent had a documented recent HIV infection, with a seroconversion time of less than 12 months. The median CD4 + T lymphocyte count and VL were 531 cells/mm 3 and 17,746 copies/mL, respectively. Considering the surveillance drug resistance mutation criteria, nine (14.1%) patients presented TDR, of which three (4.7%), five (7.8%) and four (6.2%) had protease inhibitors, resistant against nucleos(t)ide transcriptase inhibitors and against non-nucle-oside reverse-transcriptase inhibitors mutations, respectively. Two of the patients had multi-drug-resistant HIV-1. The most prevalent viral subtype was B (44, 68.8%), followed by subtype F (11, 17.2%). This study shows that TDR may vary according to the population studied and it may be higher in clusters of MSM.
The mediating role of early maladaptive schemas on relations between post-traumatic stress disorder symptom severity and illicit substance use disorder

ABSTRACT Background: This study examines the role of Early Maladaptive Schemas (EMS) on relations between Post-traumatic stress disorder (PTSD) symptoms and illicit substance use. Objectives: We assumed that EMS play a mediating role in the pathways from PTSD symptom severity to illicit substance use. Method: We used the composite score from the Addiction Severity Index to assess illicit substance use severity, the PTSD Checklist Specific to determine PTSD symptom severity, and the Young Schema Questionnaire – Short Form, Second Edition to measure the number of hyperactivated EMS. The sample consisted of 35 patients receiving social and medical care for illicit substance use problems in three French drug centers. Results: The results showed that the number of hyperactivated EMS is an intermediate variable playing a mediating role between PTSD symptom severity and illicit substance use. Results indicating full mediation. Conclusion. Future research perspectives on this question are discussed as well as the clinical repercussions of these results on the current models of management of illicit drug users.
Case Series of Brittle Cornea Syndrome

Purpose This case series demonstrate diagnostic features, treatment options, and challenges for Brittle Cornea Syndrome. Observations. Three cases presented with bluish sclera and extremely thin cornea. Genetic workup was performed and confirmed the diagnosis of Brittle Cornea Syndrome, a rare autosomal recessive disorder characterized by corneal thinning and blue sclera. Case 1 was a 4-year-old boy who developed cataract and glaucoma after undergoing right tectonic penetrating keratoplasty (PK) secondary to a spontaneous corneal rupture. Glaucoma was controlled medically. Later, the kid underwent right transcorneal lensectomy and vitrectomy with synechiolysis. After 6 weeks, he sustained graft dehiscence that was repaired using onlay patch graft. Case 2 was a 7-year-old boy who underwent PK in the right eye, then a pericardial patch graft in the left eye following spontaneous corneal rupture. Glaucoma in both eyes was controlled medically. Case 3 was the 2-year-old sister of the 2nd case. She had a pachymetry of 238 μm OD and 254 μm OD and 254 Conclusions Long-term follow-up of children diagnosed with Brittle Cornea Syndrome is paramount to minimize the morbidity of corneal rupture and late-onset extraocular conditions.
Tongue thickness and its clinical significance

Background: The tongue occupies most of the space in the oral cavity and it plays an important role in oral functions such as mastication, swallowing and articulation. The tongue continues to move from the time of intake to swallowing, particularly during masticating. A method for evaluating tongue thickness at rest by ultrasonography has been proposed; however, the association between tongue thickness and various oral functions remains unclear. Objective: The purpose of this study was to investigate the relation between tongue thickness and oral functions, and to clarify the clinical significance of tongue thickness measurements in oral function maintenance. Materials and Methods: Elderly outpatients were serially screened for enrollment and a total of 106 subjects (men, 54; women, 52; mean age, 75.2±6.5 years) were selected. Age, body mass index, and functional teeth number including implant and pontic of fixed partial denture number were recorded as the basic attributes. Tongue thickness, tongue pressure, tongue thrust pressure and tongue motor function were measured as tongue assessments. Cheek pressure, oral moisture and occlusal force were measured as other oral functions. Results: Subjects with thick tongue tended to have higher BMI, stronger muscle strengths and lower diadochokinesis. Diadochokinesis of/ka/, cheek pressure and functional teeth number were extracted as the independent factors affecting tongue thickness. Conclusion: Tongue thickness does not necessarily reflect oral functions in healthy elderly people except for a negative association between tongue thickness and oral diadochokinesis of /ka/. Multiple assessments of tongue would be required to evaluate oral function, and the assessment of tongue thickness might have a different clinical meaning.