Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability, Cyclicity and Bifurcation Diagrams of Polycycles in Non-Smooth Planar Vector Fields

In this paper we extend the main results about polycycles (also known as graphs) of planar smooth vector ﬁeld to planar non-smooth vector ﬁelds (also known as piecewise vector ﬁelds, or Filippov systems). The polycycles considered here may contain hyperbolic saddles, semi-hyperbolic saddles, saddle-nodes and tangential singularities of any degree. We determine when the polycycle is stable or unstable. We prove the bifurcation of at most one limit cycle in some conditions and at least one limit cycle for each singularity in other conditions. We also give the bifurcation diagram of the polycycles composed of a hyperbolic saddle and a quadratic-regular tangential singularity.
A Survey of Natural Language Generation

This article offers a comprehensive review of the research on Natural Language Generation (NLG) over the past two decades, especially in relation to data-to-text generation and text-to-text generation deep learning methods, as well as new applications of NLG technology. This survey aims to (a) give the latest synthesis of deep learning research on the NLG core tasks, as well as the architectures adopted in the field; (b) detail meticulously and comprehensively various NLG tasks and datasets, and draw attention to the challenges in NLG evaluation, focusing on different evaluation methods and their relationships; (c) highlight some future emphasis and relatively recent research issues that arise due to the increasing synergy between NLG and other artificial intelligence areas, such as computer vision, text, and computational creativity.
Maximum Entropy Population Based Training for Zero-Shot Human-AI Coordination

We study the problem of training a Reinforcement Learning (RL) agent that is collaborative with humans without using human data. Although such agents can be obtained through self-play training, they can suffer significantly from the distributional shift when paired with unencountered partners, such as humans. In this paper, we propose Maximum Entropy Population-based training (MEP) to mitigate such distributional shift. In MEP, agents in the population are trained with our derived Population Entropy bonus to promote the pairwise diversity between agents and the individual diversity of agents themselves. After obtaining this diversified population, a common best agent is trained by paring with agents in this population via prioritized sampling, where the prioritization is dynamically adjusted based on the training progress. We demonstrate the effectiveness of our method MEP, with comparison to Self-Play PPO (SP), Population-Based Training (PBT), Trajectory Diversity (TrajeDi), and Fictitious Co-Play (FCP) in both matrix game and Overcooked game environments, with partners being human proxy models and real humans. A supplementary video showing experimental results is available at https://youtu.be/Xh-FKD0AAKE.
Effects of uncertainty on scheduling of highway construction projects

Time and cost overruns in highway construction are usual in Colombia. Contractors regularly fail in cost estimation and construction scheduling. An analysis of important factors on time and cost overruns in highway construction in southwest Colombia is presented in this paper. Forty highway construction projects were reviewed. Data from construction documents were collected to prepare a data base for probabilistic analysis. Data collected include scheduled construction time and cost, actual construction time and cost, and unexpected events which could have affected the construction process. Data were used in a probabilistic cost and schedule risk analysis to identify critical factors in construction time and cost overruns in the region. It was found that the activities that most affect the time and cost overruns are the construction of the granular base and the asphalt layer. The most common causes of construction overruns were found to be poor cost and duration estimates, rain, and materials supply problems.
EyePAD++: A Distillation-based approach for joint Eye Authentication and Presentation Attack Detection using Periocular Images

A practical eye authentication (EA) system targeted for edge devices needs to perform authentication and be robust to presentation attacks, all while remaining compute and latency efficient. However, existing eye-based frameworks a) perform authentication and Presentation Attack Detection (PAD) independently and b) involve significant pre-processing steps to extract the iris region. Here, we introduce a joint framework for EA and PAD using periocular images. While a deep Multitask Learning (MTL) network can perform both the tasks, MTL suffers from the forgetting effect since the training datasets for EA and PAD are disjoint. To overcome this, we propose Eye Authentication with PAD (EyePAD), a distillation-based method that trains a single network for EA and PAD while reducing the effect of forgetting. To further improve the EA performance, we introduce a novel approach called EyePAD++ that includes training an MTL network on both EA and PAD data, while distilling the ‘versatility’ of the EyePAD network through an additional distillation step. Our proposed methods outperform the SOTA in PAD and obtain near-SOTA performance in eye-to-eye verification, without any pre-processing. We also demonstrate the efficacy of EyePAD and EyePAD++ in user-to-user verification with PAD across network backbones and image quality.
Asthma and vitamin D in Brazilian adolescents: Study of Cardiovascular Risks in Adolescents (ERICA)

ABSTRACT Objective: To evaluate the association between asthma prevalence and serum levels of vitamin D in Brazilian adolescents. Methods: This was a cross-sectional, school-based study involving adolescents between 12-17 years of age from four large Brazilian cities located at different latitudes (Fortaleza, Rio de Janeiro, Brasília, and Porto Alegre). Information on asthma diagnosis, lifestyle, and sociodemographic characteristics was collected by means of self-administered questionnaires. Serum concentrations of calcifediol were dichotomized as sufficient (≥ 20 ng/mL) or insufficient/deficient (< 20 ng/mL) levels. Bivariate analyses were carried out between vitamin D levels and prevalence of active asthma (AA), as well as other variables in study, using the chi-square test. Generalized linear models were configured to analyze potential confounding factors (p < 0.20). Results: Between 2013 and 2014, 1,053 adolescents were evaluated. The prevalences of AA and insufficient/deficient levels of calcifediol were 15.4% and 21%, respectively. There were no statistically significant associations between AA and hypovitaminosis D. The prevalences of AA and vitamin D insufficiency were, respectively, 2.34 (95% CI, 1,28-4.30) and 3.22 (95% CI, 1.75-5.95) times higher in Porto Alegre than in Rio de Janeiro, regardless of possible confounding factors. However, no significant associations were found between the prevalence of AA and vitamin-D-related variables in any of the cities. Conclusions: No association was found between AA and low levels of vitamin D in adolescents living at different latitudes in Brazil.
Shorting pin loaded dual-band compact rectangular microstrip antenna

Theoretical and experimental investigations carried out on shorted microstrip patch antenna for dual band operation. The investigations were carried out by varying the shorting-pin position from the edge to the centre of the patch; such an antenna provides a frequency tunability range from 0.88 GHz to 1.08 GHz for first resonance and from 2.20 GHz to 2.59 GHz for second resonance. A frequency ratio of about 2.91 to 2.2 for the two operating frequencies is observed. When the shorting-pin position is close to centre or at the centre of the patch, a single resonant frequency is observed. It is also observed that the resonant frequency of the antenna heavily depends upon the thickness of the substrate, dielectric constant of the substrate and radius of the shorting-pin.
Optimal Portfolio Choice and Stock Centrality for Tail Risk Events

We propose a novel risk matrix to characterize the optimal portfolio choice of an investor with tail concerns. The diagonal of the matrix contains the Value-at-Risk of each asset in the portfolio and the off-diagonal the pairwise Delta-CoVaR measures reflecting tail connections between assets. First, we derive the conditions under which the associated quadratic risk function has a closed-form solution. Second, we examine the relationship between portfolio risk and eigenvector centrality. Third, we show that portfolio risk is not necessarily increasing with respect to stock centrality. Forth, we demonstrate under certain conditions that asset centrality increases the optimal weight allocation of the asset to the portfolio. Overall, our empirical study indicates that a network topology which exhibits low connectivity is outperformed by high connectivity based on a Sharpe ratio test.
BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View

Autonomous driving perceives its surroundings for decision making, which is one of the most complex scenarios in visual perception. The success of paradigm innovation in solving the 2D object detection task inspires us to seek an elegant, feasible, and scalable paradigm for fundamentally pushing the performance boundary in this area. To this end, we contribute the BEVDet paradigm in this paper. BEVDet performs 3D object detection in Bird-Eye-View (BEV), where most target values are defined and route planning can be handily performed. We merely reuse existing modules to build its framework but substantially develop its performance by constructing an exclusive data augmentation strategy and upgrading the Non-Maximum Suppression strategy. In the experiment, BEVDet offers an excellent trade-off between accuracy and time-efficiency. As a fast version, BEVDet-Tiny scores 31.2% mAP and 39.2% NDS on the nuScenes val set. It is comparable with FCOS3D, but requires just 11% computational budget of 215.3 GFLOPs and runs 9.2 times faster at 15.6 FPS. Another high-precision version dubbed BEVDet-Base scores 39.3% mAP and 47.2% NDS, significantly exceeding all published results. With a comparable inference speed, it surpasses FCOS3D by a large margin of +9.8% mAP and +10.0% NDS. The source code is publicly available for further research at https://github.com/HuangJunJie2017/BEVDet .
Analytical Modeling of Exoplanet Transit Spectroscopy with Dimensional Analysis and Symbolic Regression

The physical characteristics and atmospheric chemical composition of newly discovered exoplanets are often inferred from their transit spectra, which are obtained from complex numerical models of radiative transfer. Alternatively, simple analytical expressions provide insightful physical intuition into the relevant atmospheric processes. The deep-learning revolution has opened the door for deriving such analytical results directly with a computer algorithm fitting to the data. As a proof of concept, we successfully demonstrate the use of symbolic regression on synthetic data for the transit radii of generic hot-Jupiter exoplanets to derive a corresponding analytical formula. As a preprocessing step, we use dimensional analysis to identify the relevant dimensionless combinations of variables and reduce the number of independent inputs, which improves the performance of the symbolic regression. The dimensional analysis also allowed us to mathematically derive and properly parameterize the most general family of degeneracies among the input atmospheric parameters that affect the characterization of an exoplanet atmosphere through transit spectroscopy.
Semismoothness for Solution Operators of Obstacle-Type Variational Inequalities with Applications in Optimal Control

We prove that solution operators of elliptic obstacle-type variational inequalities (or, more generally, locally Lipschitz continuous functions possessing certain pointwise-a.e. convexity properties) are Newton differentiable when considered as maps between suitable Lebesgue spaces and equipped with the strong-weak Bouligand differential as a generalized set-valued derivative. It is shown that this Newton differentiability allows to solve optimal control problems with H1-cost terms and one-sided pointwise control constraints by means of a semismooth Newton method. The superlinear convergence of the resulting algorithm is proved in the infinite-dimensional setting and its mesh independence is demonstrated in numerical experiments. We expect that the findings of this paper are also helpful for the design of numerical solution procedures for quasi-variational inequalities and the optimal control of obstacle-type variational problems.
Graph augmented Deep Reinforcement Learning in the GameRLand3D environment

We address planning and navigation in challenging 3D video games featuring maps with disconnected regions reachable by agents using special actions. In this setting, classical symbolic planners are not applicable or difficult to adapt. We introduce a hybrid technique combining a low level policy trained with reinforcement learning and a graph based high level classical planner. In addition to providing human-interpretable paths, the approach improves the generalization performance of an end-to-end approach in unseen maps, where it achieves a 20% absolute increase in success rate over a recurrent end-to-end agent on a point to point navigation task in yet unseen large-scale maps of size 1km x 1km. In an in-depth experimental study, we quantify the limitations of end-to-end Deep RL approaches in vast environments and we also introduce"GameRLand3D", a new benchmark and soon to be released environment can generate complex procedural 3D maps for navigation tasks.
Clinical implication of adjuvant chemotherapy according to mismatch repair status in patients with intermediate-risk stage II colon cancer: a retrospective study

Background The present study evaluated the clinical implications of adjuvant chemotherapy according to the mismatch repair (MMR) status and clinicopathologic features of patients with intermediate- and high-risk stage II colon cancer (CC). Methods This study retrospectively reviewed 5,774 patients who were diagnosed with CC and underwent curative surgical resection at Kyungpook National University Chilgok Hospital. The patients were enrolled according to the following criteria: (1) pathologically diagnosed with primary CC; (2) stage II CC classified based on the 7th edition of the American Joint Committee on Cancer staging system; (3) intermediate- and high-risk features; and (4) available test results for MMR status. A total of 286 patients met these criteria and were included in the study. Results Among the 286 patients, 54 (18.9%) were identified as microsatellite instability-high (MSI-H) or deficient MMR (dMMR). Although all the patients identified as MSI-H/dMMR showed better survival outcomes, T4 tumors and adjuvant chemotherapy were identified as independent prognostic factors for survival. For the intermediate-risk patients identified as MSI-low (MSI-L)/microsatellite stable (MSS) or proficient MMR (pMMR), adjuvant chemotherapy exhibited a significantly better disease-free survival (DFS) but had no impact on overall survival (OS). Oxaliplatin-containing regimens showed no association with DFS or OS. Adjuvant chemotherapy was not associated with DFS in intermediate-risk patients identified as MSI-H/dMMR. Conclusion The current study found that the use of adjuvant chemotherapy was correlated with better DFS in MSI-L/MSS or pMMR intermediate-risk stage II CC patients.
An Alternate Policy Gradient Estimator for Softmax Policies

Policy gradient (PG) estimators are ineﬀec-tive in dealing with softmax policies that are sub-optimally saturated, which refers to the situation when the policy concentrates its probability mass on sub-optimal actions. Sub-optimal policy saturation may arise from bad policy initialization or sudden changes in the environment that occur after the policy has already converged. Current softmax PG estimators require a large number of updates to overcome policy saturation, which causes low sample eﬃciency and poor adaptability to new situations. To mitigate this problem, we propose a novel PG estimator for softmax policies that utilizes the bias in the critic estimate and the noise present in the reward signal to escape the saturated regions of the policy parameter space. Our theoretical analysis and experiments, conducted on bandits and various reinforcement learning environments, show that this new estimator is signiﬁcantly more robust to policy saturation. for the gradient bandit estimators with true r π and an additional normally distributed gradient noise. The individual graph labels (0 , 0 . 1 , 0 . 5 , 1 , 2) show the standard deviation of the normal noise added to the gradient. The subplot headers show the policy saturation at the beginning of the experiment.
Barely-Supervised Learning: Semi-Supervised Learning with very few labeled images

This paper tackles the problem of semi-supervised learning when the set of labeled samples is limited to a small number of images per class, typically less than 10, problem that we refer to as barely-supervised learning. We analyze in depth the behavior of a state-of-the-art semi-supervised method, FixMatch, which relies on a weakly-augmented version of an image to obtain supervision signal for a more strongly-augmented version. We show that it frequently fails in barely-supervised scenarios, due to a lack of training signal when no pseudo-label can be predicted with high confidence. We propose a method to leverage self-supervised methods that provides training signal in the absence of confident pseudo-labels. We then propose two methods to refine the pseudo-label selection process which lead to further improvements.The first one relies on a per-sample history of the model predictions, akin to a voting scheme. The second iteratively up-dates class-dependent confidence thresholds to better explore classes that are under-represented in the pseudo-labels. Our experiments show that our approach performs significantly better on STL-10 in the barely-supervised regime,e.g. with 4 or 8 labeled images per class.
Probing Vainsthein-screening gravity with galaxy clusters using internal kinematics and strong and weak lensing

We use high-precision combined strong/weak lensing and kinematics measurements of the total mass profiles of the observed galaxy clusters MACS J1206.2-0847 and Abell S1063, to constrain the relativistic sector of the general DHOST dark energy theories, which exhibit a partial breaking of the so calledVainsthein screeningmechanism, on the linear level of scalar fluctuations around a cosmological background. In particular, by using theMG-MAMMPOSSt framework developed in Pizzuti et al. 2021, for the kinematics analysis of member galaxies in clusters along with lensing mass profile reconstructions we provide new constraints on the coupling Y2 which governs the theory’s relativistic contribution to the lensing potential. The new bound from the combination of kinematics and lensing measurements of MACS 1206, Y2 = −0.12+0.66 −0.67 at 2σ, provides about a 2-fold improvement on previous constraints. In the case of Abell S1063 a > 2σ tension with the GR expectation arises. We discuss this in some detail, and we investigate the possible sources of systematics which can explain the tension. We further discuss why the combination of kinematics of member galaxies with lensing is capable of providing much tighter bounds compared to kinematics or lensing alone, and we explain how the number density profile of tracers, as well as the choice of the velocity anisotropy profile affects the final results.
On a Linear Gromov–Wasserstein Distance

Gromov–Wasserstein distances are generalization of Wasserstein distances, which are invariant under distance preserving transformations. Although a simplified version of optimal transport in Wasserstein spaces, called linear optimal transport (LOT), was successfully used in practice, there does not exist a notion of linear Gromov–Wasserstein distances so far. In this paper, we propose a definition of linear Gromov–Wasserstein distances. We motivate our approach by a generalized LOT model, which is based on barycentric projection maps of transport plans. Numerical examples illustrate that the linear Gromov–Wasserstein distances, similarly as LOT, can replace the expensive computation of pairwise Gromov–Wasserstein distances in applications like shape classification.
Exploring Inter-frequency Guidance of Image for Lightweight Gaussian Denoising

Image denoising is of vital importance in many imaging or computer vision related areas. With the convolutional neural networks showing strong capability in computer vision tasks, the performance of image denoising has also been brought up by CNN based methods. Though CNN based image denoisers show promising results on this task, most of the current CNN based methods try to learn the mapping from noisy image to clean image directly, which lacks the explicit exploration of prior knowledge of images and noises. Natural images are observed to obey the reciprocal power law, implying the low-frequency band of image tend to occupy most of the energy. Thus in the condition of AGWN (additive gaussian white noise) deterioration, low-frequency band tend to preserve a higher PSNR than high-frequency band. Considering the spatial morphological consistency of different frequency bands, low-frequency band with more fidelity can be used as a guidance to refine the more contaminated high-frequency bands. Based on this thought, we proposed a novel network architecture denoted as IGNet, in order to refine the frequency bands from low to high in a progressive manner. Firstly, it decomposes the feature maps into high- and low-frequency subbands using DWT (discrete wavelet transform) iteratively, and then each low band features are used to refine the high band features. Finally, the refined feature maps are processed by a decoder to recover the clean result. With this design, more inter-frequency prior and information are utilized, thus the model size can be lightened while still perserves competitive results. Experiments on several public datasets show that our model obtains competitive performance comparing with other state-of-the-art methods yet with a lightweight structure.
Travel Guides for Creative Tourists, Powered by Geotagged Social Media

Many modern tourists want to know about everyday life and spend time like a local in a new city. Current tools and guides typically provide them with lists of sights to see, which do not meet their needs. Manually building new tools for them would not scale. However, public geotagged social media data, like tweets and photos, have the potential to fill this gap, showing users an interesting and unique side of a place. Through three studies surrounding the design and construction of a social-media-powered Neighborhood Guides website, we show recommendations for building such a site. Our findings highlight an important aspect of social media: while it lacks the user base and consistency to directly reflect users’ lives, it does reveal the idealized everyday life that so many visitors want to know about. ACM Classification