Miracle-dz/UniFAR-FTUs huggingface dataset:
Texture Hallucination for Large-Scale Painting Super-Resolution

We aim to super-resolve digital paintings, synthesizing realistic details from high-resolution reference painting materials for very large scaling factors (e.g., 8x, 16x). However, previous single image super-resolution (SISR) methods would either lose textural details or introduce unpleasing artifacts. On the other hand, reference-based SR (Ref-SR) methods can transfer textures to some extent, but is still impractical to handle very large scales and keep fidelity with original input. To solve these problems, we propose an efficient high-resolution hallucination network for very large scaling factors with efficient network structure and feature transferring. To transfer more detailed textures, we design a wavelet texture loss, which helps to enhance more high-frequency components. At the same time, to reduce the smoothing effect brought by the image reconstruction loss, we further relax the reconstruction constraint with a degradation loss which ensures the consistency between downscaled super-resolution results and low-resolution inputs. We also collected a high-resolution (e.g., 4K resolution) painting dataset PaintHD by considering both physical size and image resolution. We demonstrate the effectiveness of our method with extensive experiments on PaintHD by comparing with SISR and Ref-SR state-of-the-art methods.
A new generalisation of the directed last passage percolation: its study on the cylinders

The directed last passage percolation (LPP) on the quarter-plane is a growing model. To come into the growing set, a cell needs that the cells on its bottom and on its left to be in the growing set, and then to wait a random time. We present here a new generalisation of directed last passage percolation (GLPP). In GLPP, the waiting time of a cell depends on the difference of the coming times of its bottom and left cells. We explain in this article the physical meaning of this generalisation. In this first work on GLPP, we study them as a growing model on the cylinders rather than on the quarter-plane, the eighth-plane or the half-plane. We focus, mainly, on the law of the front line. In particular, we prove, in some integrable cases, that this law could be given explicitly as a function of the parameters of the model. Moreover, our results applied to LPP are the first ones for LPP on the cylinders. These new results are obtained by the use of probabilistic cellular automata (PCA) to study LPP and GLPP.
Solving Jigsaw Puzzles With Eroded Boundaries

Jigsaw puzzle solving is an intriguing problem which has been explored in computer vision for decades. This paper focuses on a specific variant of the problem—solving puzzles with eroded boundaries. Such erosion makes the problem extremely difficult, since most existing solvers utilize solely the information at the boundaries. Nevertheless, this variant is important since erosion and missing data often occur at the boundaries. The key idea of our proposed approach is to inpaint the eroded boundaries between puzzle pieces and later leverage the quality of the inpainted area to classify a pair of pieces as ”neighbors or not”. An interesting feature of our architecture is that the same GAN discriminator is used for both inpainting and classification; training of the second task is simply a continuation of the training of the first, beginning from the point it left off. We show that our approach outperforms other SOTA methods.
Dynamic Spatial Verification for Large-Scale Object-Level Image Retrieval

Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks. Researchers working onContent-Based Image Retrieval (CBIR) have traditionally tuned their algorithms to match filtered results with user search intent. However, we are now bombarded with composite images of unknown origin, authenticity, and even meaning. With such uncertainty, users may not have an initial idea of what the results of a search query should look like. For instance, hidden people, spliced objects, and subtly altered scenes can be difficult for a user to detect initially in a meme image, but may contribute significantly to its composition. We propose a new approach for spatial verification that aims at modeling object-level regions dynamically clustering keypoints in a 2D Hough space, which are then used to accurately weight small contributing objects within the results, without the need for costly object detection steps. We call this method Objects in Scene to Objects in Scene (OS2OS) score, and it is optimized for fast matrix operations on CPUs. OS2OS performs comparably to state-of-the-art methods in classic CBIR problems, on the Oxford5K, Paris 6K, and Google-Landmarks datasets, without the need for bounding boxes. It also succeeds in emerging retrieval tasks such as image composite matching in the NIST MFC2018 dataset and meme-style composite imagery fromReddit.
Survival and secondary malignancy rates for adjuvant radiation therapy versus observation in stage I testicular seminoma: A Surveillance, Epidemiology, and End Results (SEER) analysis

5020 Background: The standard adjuvant treatment for men with stage I testicular seminoma remains controversial within the literature. We analyzed survival and secondary malignancy rates in men with stage I seminoma who underwent adjuvant radiation therapy (RT) or observation (OB) following orchiectomy. Methods: Data was obtained from the Surveillance, Epidemiology, and End Results program (SEER) of the U.S. National Cancer Institute from 1973 - 2003. The primary end points were overall survival (OS) and cause-specific survival (CSS). Multivariate Cox regression models were utilized to study the significance of clinical variables: age at diagnosis, laterality of primary disease, race (white, black, American Indian, Asian/pacific islander) and radiation group. Standardized incidence ratios (observed-to-expected [O/E] ratio) and absolute excess risks of secondary malignancies were also assessed for each subgroup. Results: Out of 6,764 patients eligible for analysis, 5,265 were treated with RT and 1,499 with.
Continuum Enhancements, Line Profiles, and Magnetic Field Evolution during Consecutive Flares

During solar flares, magnetic energy can be converted into electromagnetic radiation from radio waves to γ-rays. Enhancements in the continuum at visible wavelengths, as well as continuum enhancements in the FUV and NUV passbands, give rise to white-light flares. In addition, the strong energy release in these events can lead to the rearrangement of the magnetic field at the photospheric level, causing morphological changes in large and stable magnetic structures like sunspots. In this context, we describe observations acquired by satellite instruments (Interface Region Imaging Spectrograph (IRIS), Solar Dynamics Observatory/Helioseismic and Magnetic Imager, Hinode/Solar Optical Telescope) and ground-based telescopes (Rapid Oscillations in the Solar Atmosphere (ROSA)/Dunn Solar Telescope) during two consecutive C7.0 and X1.6 flares that occurred in active region NOAA 12205 on 2014 November 7. The flare was accompanied by an eruption. The results of the analysis show the presence of continuum enhancements during the evolution of the events, observed both in ROSA images and in IRIS spectra. In the latter, a prominent blueshifted component is observed at the onset of the eruption. We investigate the role played by the evolution of the δ sunspots of the active region in the flare triggering, and finally we discuss the changes in the penumbrae surrounding these sunspots as a further consequence of these flares.
Efficient adjustment sets for population average treatment effect estimation in non-parametric causal graphical models

The method of covariate adjustment is often used for estimation of population average treatment effects in observational studies. Graphical rules for determining all valid covariate adjustment sets from an assumed causal graphical model are well known. Restricting attention to causal linear models, a recent article derived two novel graphical criteria: one to compare the asymptotic variance of linear regression treatment effect estimators that control for certain distinct adjustment sets and another to identify the optimal adjustment set that yields the least squares treatment effect estimator with the smallest asymptotic variance among consistent adjusted least squares estimators. In this paper we show that the same graphical criteria can be used in non-parametric causal graphical models when treatment effects are estimated by contrasts involving non-parametrically adjusted estimators of the interventional means. We also provide a graphical criterion for determining the optimal adjustment set among the minimal adjustment sets, which is valid for both linear and non-parametric estimators. We provide a new graphical criterion for comparing time dependent adjustment sets, that is, sets comprised by covariates that adjust for future treatments and that are themselves affected by earlier treatments. We show by example that uniformly optimal time dependent adjustment sets do not always exist. In addition, for point interventions, we provide a sound and complete graphical criterion for determining when a non-parametric optimally adjusted estimator of an interventional mean, or of a contrast of interventional means, is as efficient as an efficient estimator of the same parameter that exploits the information in the conditional independencies encoded in the non-parametric causal graphical model.
LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation

Current 6D object pose estimation methods usually require a 3D model for each object. These methods also require additional training in order to incorporate new objects. As a result, they are difficult to scale to a large number of objects and cannot be directly applied to unseen objects. We propose a novel framework for 6D pose estimation of unseen objects. We present a network that reconstructs a latent 3D representation of an object using a small number of reference views at inference time. Our network is able to render the latent 3D representation from arbitrary views. Using this neural renderer, we directly optimize for pose given an input image. By training our network with a large number of 3D shapes for reconstruction and rendering, our network generalizes well to unseen objects. We present a new dataset for unseen object pose estimation--MOPED. We evaluate the performance of our method for unseen object pose estimation on MOPED as well as the ModelNet and LINEMOD datasets. Our method performs competitively to supervised methods that are trained on those objects. Code and data will be available at https://keunhong.com/publications/latentfusion/
Predicting Solar Flares with Machine Learning: Investigating Solar Cycle Dependence

A deep learning network, long short-term memory (LSTM), is used to predict whether an active region (AR) will produce a flare of class Γ in the next 24 hr. We consider Γ to be ≥M (strong flare), ≥C (medium flare), and ≥A (any flare) class. The essence of using LSTM, which is a recurrent neural network, is its ability to capture temporal information on the data samples. The input features are time sequences of 20 magnetic parameters from the space weather Helioseismic and Magnetic Imager AR patches. We analyze ARs from 2010 June to 2018 December and their associated flares identified in the Geostationary Operational Environmental Satellite X-ray flare catalogs. Our results produce skill scores consistent with recently published results using LSTMs and are better than the previous results using a single time input. The skill scores from the model show statistically significant variation when different years of data are chosen for training and testing. In particular, 2015–2018 have better true skill statistic and Heidke skill scores for predicting ≥C medium flares than 2011–2014, when the difference in flare occurrence rates is properly taken into account.
Fast Stochastic Ordinal Embedding With Variance Reduction and Adaptive Step Size

Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (<italic>SDP</italic>), which is generally time-consuming and degrades the scalability, especially confronting large-scale data. To overcome this challenge, we propose a stochastic algorithm called <italic>SVRG-SBB</italic>, which has the following features: i) achieving good scalability via dropping positive semi-definite (<italic>PSD</italic>) constraints as serving a fast algorithm, i.e., stochastic variance reduced gradient (<italic>SVRG</italic>) method, and ii) adaptive learning via introducing a new, adaptive step size called the stabilized Barzilai-Borwein (<italic>SBB</italic>) step size. Theoretically, under some natural assumptions, we show the <inline-formula><tex-math notation="LaTeX">$\boldsymbol{O}(\frac{1}{T})$</tex-math> <alternatives><mml:math><mml:mrow><mml:mi mathvariant="bold">O</mml:mi><mml:mo>(</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mi>T</mml:mi></mml:mfrac><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="ma-ieq1-2956700.gif"/></alternatives></inline-formula> rate of convergence to a stationary point of the proposed algorithm, where <inline-formula><tex-math notation="LaTeX">$T$</tex-math> <alternatives><mml:math><mml:mi>T</mml:mi></mml:math><inline-graphic xlink:href="ma-ieq2-2956700.gif"/></alternatives></inline-formula> is the number of total iterations. Under the further Polyak-Łojasiewicz assumption, we can show the global linear convergence (i.e., exponentially fast converging to a global optimum) of the proposed algorithm. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm by comparing with the state-of-the-art methods, notably, much lower computational cost with good prediction performance.
ACE – An Anomaly Contribution Explainer for Cyber-Security Applications

In this paper we introduce Anomaly Contribution Explainer or ACE, a tool to explain security anomaly detection models in terms of the model features through a regression framework, and its variant, ACE-KL, which highlights the important anomaly contributors. ACE and ACE-KL provide insights in diagnosing which attributes significantly contribute to an anomaly by building a specialized linear model to locally approximate the anomaly score that a black-box model generates. We conducted experiments with these anomaly detection models to detect security anomalies on both synthetic data and real data. In particular, we evaluate performance on three public data sets: CERT insider threat, netflow logs, and Android malware. The experimental results are encouraging: our methods consistently identify the correct contributing feature in the synthetic data where ground truth is available; similarly, for real data sets, our methods point a security analyst in the direction of the underlying causes of an anomaly, including in one case leading to the discovery of previously overlooked network scanning activity. We have made our source code publicly available.
Theoretical Atto-nano Physics

Two emerging areas of research, attosecond and nanoscale physics, have recently started to merge. Attosecond physics deals with phenomena occurring when ultrashort laser pulses, with duration on the femto- and sub-femtosecond time scales, interact with atoms, molecules or solids. The laser-induced electron dynamics occurs natively on a timescale down to a few hundred or even tens of attoseconds (1 attosecond=1 as=10$^{-18}$ s), which is of the order of the optical field cycle. For comparison, the revolution of an electron on a $1s$ orbital of a hydrogen atom is $\sim152$ as. On the other hand, the second topic involves the manipulation and engineering of mesoscopic systems, such as solids, metals, and dielectrics, with nanometric precision. Although nano-engineering is a vast and well-established research field on its own, the combination with intense laser physics is relatively recent. We present a comprehensive theoretical overview of the tools to tackle and understand the physics that takes place when short and intense laser pulses interact with nanosystems, such as metallic and dielectric nanostructures. In particular, we elucidate how the spatially inhomogeneous laser-induced fields at a nanometer scale modify the laser-driven electron dynamics. Consequently, this has an important impact on pivotal processes such as above-threshold ionization and high-order harmonic generation. The deep understanding of the coupled dynamics between these spatially inhomogeneous fields and matter configures a promising way to new avenues of research and applications. Thanks to the maturity that attosecond physics has reached, together with the tremendous advance in material engineering and manipulation techniques, the age of atto-nano physics has begun, but it is still in an incipient stage.
Error-Correcting Neural Network

Though deep learning has been applied successfully in many scenarios, malicious inputs with human-imperceptible perturbations can make it vulnerable in real applications. This paper proposes an error-correcting neural network (ECNN) that combines a set of binary classifiers to combat adversarial examples in the multi class classification problem. To build an ECNN, we propose to design a code matrix so that the minimum Hamming distance between any two rows (i.e., two codewords) and the minimum shared information distance between any two columns (i.e., two partitions of class labels) are simultaneously maximized. Maximizing row distances can increase the system fault tolerance while maximizing column distances helps increase the diversity between binary classifiers. We propose an end-to-end training method for our ECNN, which allows further improvement of the diversity between binary classifiers. The end-to-end training renders our proposed ECNN different from the traditional error-correcting output code (ECOC) based methods that train binary classifiers independently. We empirically demonstrate that our proposed ECNN is effective against the state-of-the-art white-box attacks while maintaining good classification accuracy on normal examples.
Optimize TSK Fuzzy Systems for Classification Problems: Minibatch Gradient Descent With Uniform Regularization and Batch Normalization

Takagi–Sugeno–Kang (TSK) fuzzy systems are flexible and interpretable machine learning models; however, they may not be easily optimized when the data size is large, and/or the data dimensionality is high. This article proposes a minibatch gradient descent (MBGD) based algorithm to efficiently and effectively train TSK fuzzy classifiers. It integrates two novel techniques: First, uniform regularization (UR), which forces the rules to have similar average contributions to the output, and hence to increase the generalization performance of the TSK classifier; and, second, batch normalization (BN), which extends BN from deep neural networks to TSK fuzzy classifiers to expedite the convergence and improve the generalization performance. Experiments on 12 UCI datasets from various application domains, with varying size and dimensionality, demonstrated that UR and BN are effective individually, and integrating them can further improve the classification performance.
Utility of paediatric quality of life and revised illness perception questionnaires in adolescents with sickle cell disease

ABSTRACT Quality of life (QOL) and illness perceptions are important considerations when managing adolescents with sickle cell disease (SCD). This study aims to assess the psychometric properties of the SCD-specific paediatric quality of life (PedsQL™-SCD) and the revised illness perception questionnaires (IPQ-R) in Jamaican adolescents with SCD. One hundred and fifty Jamaican adolescents (M: 74, F: 76; mean age 16.1 ± 1.9 years, range: 13–19 years) completed the IPQ-R, PedsQL™-SCD (teen and parent forms) and a knowledge questionnaire. Socio-demographic and clinical data were also collected and the latter were used to create a severity index. Instruments’ structure, feasibility, reliability and construct validity were assessed. The Cronbach’s alpha for the 24-item knowledge questionnaire was 0.85; IPQ-R domains from 0.56 to 0.79 (except ‘treatment control’: 0.34); PedsQL teen-form domains from 0.70 to 0.93 and PedsQL parent-form domains from 0.72 to 0.95. All scales and subscales had low floor effects but some teen subscales yielded high ceiling effects. The SCD-specific scale and subscales showed high sensitivity as they varied significantly by severity of illness. Factor analysis of causal items on IPQ-R yielded a three-factor structure, explaining a total variance of 52.1%. The factors could be labelled as ‘psychological’, ‘physical’ and ‘innate/behavioural risk factors’. The teen QOL scores had significant negative correlations with the severity score (r: −0.46, p-value <0.001); IPQ identity domain (r: −0.42, p-value <0.001); IPQ consequences subscale (r: −0.36, p-value <0.001); IPQ cyclical timeline subscale (r: −0.31, p-value <0.001) and IPQ emotional representation subscale (r: −0.29, p-value <0.01). The severity index also had significant correlation with the QOL parent form (r: −0.44, p-value <0.001); IPQ identity domain (r: 0.43, p-value <0.01) and IPQ consequences domain (r: 0.35, p-value <0.001). In conclusion, both the IPQ-R and PedsQL™-SCD demonstrate robust psychometric properties in adolescents with SCD. Continuing longitudinal study will provide further assessment of the instruments such as ‘responsiveness to change’.
Supplementation of female calves in creep feeding system and productive and nutritional parameters of beef cows on tropical pasture

Objetivou-se avaliar o desempenho produtivo e nutricional de bezerras lactentes recebendo mistura mineral ou suplementos multiplos e a producao de leite, o consumo e a digestibilidade do pasto consumido por vacas de corte, em pastagens de Uruchloa decumbens . Utilizaram-se 48 bezerras de corte, lactentes, com idade media de 5 meses e peso medio inicial de 125,4 + 1,34 kg, e suas respectivas maes com peso inicial medio de 435,2 + 10,3 kg. O delineamento experimental foi o inteiramente casualizado. Os suplementos continham aproximadamente 25% de proteina bruta (PB) e substituicao progressiva do farelo de soja (FS) pelo farelo de algodao 38% (FA) aos niveis de 0; 33; 67 e 100% para os tratamentos FA0, FA33, FA67 e FA100, respectivamente. Adicionou-se ureia aos suplementos visando ajustar o teor proteico dos suplementos. Os animais do tratamento controle (MM) receberam exclusivamente mistura mineral ad libitum ; nos demais tratamentos os suplementos multiplos foram fornecidos na quantidade de 0,5 kg/animal/dia. O ganho medio diario (g) foi 687,8; 733,2; 820,0; 760,6 e 764,5, respectivamente, para os tratamentos MM, FA0, FA33, FA67 e FA100. As bezerras suplementadas apresentaram maior ganho em peso. Nao se verificou efeito dos niveis de FA nos suplementos multiplos (P>0,10) sobre o ganho medio diario. Os consumos em kg/dia de materia seca (MS), materia orgânica (MO), proteina bruta (PB), carboidratos nao fibrosos (CNF), MS digerida e nutrientes digestiveis totais foram superiores (P 0,10). Conclui-se que a utilizacao de suplementos multiplos no periodo pre-desmama, propicia maior ganho de peso as bezerras.