Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncertainty-Driven Loss for Single Image Super-Resolution

In low-level vision such as single image super-resolution (SISR), traditional MSE or L 1 loss function treats every pixel equally with the assumption that the importance of all pixels is the same. However, it has been long recognized that texture and edge areas carry more important visual information than smooth areas in photographic images. How to achieve such spatial adaptation in a principled manner has been an open problem in both traditional model-based and modern learning-based approaches toward SISR. In this paper, we propose a new adaptive weighted loss for SISR to train deep networks focusing on challenging situations such as textured and edge pixels with high uncertainty. Speciﬁcally, we introduce variance estimation characterizing the uncertainty on a pixel-by-pixel basis into SISR solutions so the targeted pixels in a high-resolution image (mean) and their corresponding uncertainty (variance) can be learned simultaneously. Moreover, uncertainty estimation allows us to leverage conventional wisdom such as sparsity prior for regularizing SISR solutions. Ultimately, pixels with large certainty (e.g., texture and edge pixels) will be prioritized for SISR according to their importance to visual quality. For the ﬁrst time, we demonstrate that such uncertainty-driven loss can achieve better results than MSE or L 1 loss for a wide range of network architectures. Experimental results on three popular SISR networks show that our proposed uncertainty-driven loss has achieved better PSNR performance than traditional loss functions without any increased computation during testing.
The story of building a historic chapel as told by its timber

ABSTRACT Architectural features of the structure and dendrochronological analysis of its timber has disproved the previously held opinion of the history of the chapel erected in a small Karelian village. It was found that the eastern part of the building, which was thought to have been built last, is in fact the oldest part of the structure. According to dendrochronological analysis it was erected in 1745. In 1891 the western part with the bell tower was added. Later on, plank sidings were mounted and the roof was modified. The study confirmed there was a time gap between the usage of the same engineering practices among the Russian and the Karelian population of Karelia, indicating a more archaic architecture among Karelians.
Learning with Labeling Induced Abstentions

Consider a setting where we wish to automate an expensive task with a machine learning algorithm using a limited labeling resource. In such settings, examples routed for labeling are often out of scope for the machine learning algorithm. For example, in a spam detection setting, human reviewers not only provide labeled data but are such high-quality detectors of spam that examples routed to them no longer require machine evaluation. As a consequence, the distribution of examples routed to the machine is intimately tied to the process generating labels. We introduce a formalization of this setting, and give an algorithm that simultaneously learns a model and decides when to request a label by leveraging ideas from both the abstention and active learning literatures. We prove an upper bound on the algorithm’s label complexity and a matching lower bound for any algorithm in this setting. We conduct a thorough set of experiments including an ablation study to test different components of our algorithm. We demonstrate the effectiveness of an efﬁcient version of our algorithm over margin sampling on a variety of datasets.
Learning to Generate Visual Questions with Noisy Supervision

The task of visual question generation (VQG) aims to generate human-like neural questions from an image and potentially other side information (e.g., answer type or the answer itself). Existing works often suffer from the severe one image to many questions mapping problem, which generates uninformative and non-referential questions. Recent work has demonstrated that by leveraging double visual and answer hints, a model can faithfully generate much better quality questions. However, visual hints are not available naturally. Despite they proposed a simple rule-based similarity matching method to obtain candidate visual hints, they could be very noisy practically and thus restrict the quality of generated questions. In this paper, we present a novel learning approach for double-hints based VQG, which can be cast as a weakly supervised learning problem with noises. The key rationale is that the salient visual regions of interest can be viewed as a constraint to improve the generation procedure for producing high-quality questions. As a result, given the predicted salient visual regions of interest, we can focus on estimating the probability of being ground-truth questions, which in turn implicitly measures the quality of predicted visual hints. Experimental results on two benchmark datasets show that our proposed method outperforms the state-of-the-art approaches by a large margin on a variety of metrics, including both automatic machine metrics and human evaluation.
On the Value of Infinite Gradients in Variational Autoencoder Models

A number of recent studies of continuous variational autoencoder (VAE) models have noted, either directly or indirectly, the tendency of various parameter gradients to drift towards inﬁnity during training. Because such gradients could potentially contribute to numerical instabilities, and are often framed as a problematic phenomena to be avoided, it may be tempting to shift to alternative energy functions that guarantee bounded gradients. But it remains an open question: What might the unintended consequences of such a restriction be? To address this issue, we examine how unbounded gradients relate to the regularization of a broad class of autoencoder-based architectures, including VAE models, as applied to data lying on or near a low-dimensional manifold (e.g., natural images). Our main ﬁnding is that, if the ultimate goal is to simultaneously avoid over-regularization (high reconstruction errors, sometimes referred to as posterior collapse) and under-regularization (excessive latent dimensions are not pruned from the model), then an autoencoder-based energy function with inﬁnite gradients around optimal representations is provably required per a certain technical sense which we carefully detail. Given that both over-and under-regularization can directly lead to poor generated sample quality or suboptimal feature selection, this result suggests that heuristic modiﬁcations to or constraints on the VAE energy function may at times be ill-advised, and large gradients should be accommodated to the extent possible.
Dynamic Normalization and Relay for Video Action Recognition

Convolutional Neural Networks (CNNs) have been the dominant model for video action recognition. Due to the huge memory and compute demand, popular action recognition networks need to be trained with small batch sizes, which makes learning discriminative spatial-temporal representations for videos become a challenging problem. In this paper, we present Dynamic Normalization and Relay (DNR), an improved normalization design, to augment the spatial-temporal representation learning of any deep action recognition model, adapting to small batch size training settings. We observe that state-of-the-art action recognition networks usually apply the same normalization parameters to all video data, and ignore the dependencies of the estimated normalization parameters between neighboring frames (at the same layer) and between neighboring layers (with all frames of a video clip). Inspired by this, DNR introduces two dynamic normalization relay modules to explore the potentials of cross-temporal and cross-layer feature distribution dependencies for estimating accurate layer-wise normalization parameters. These two DNR modules are instantiated as a light-weight recurrent structure conditioned on the current input features, and the normalization parameters estimated from the neighboring frames based features at the same layer or from the whole video clip based features at the preceding layers. We ﬁrst plug DNR into prevailing 2D CNN backbones and test its performance on public action recognition datasets including Kinetics and Something-Something. Experimental results show that DNR brings large performance improvements to the baselines, achieving over 4.4 % absolute margins in top-1 accuracy without training bells and whistles. More experiments on 3D backbones and several latest 2D spatial-temporal networks further validate its effectiveness. Code will be available at https://github.com/caidonkey/dnr .
Co-evolution Transformer for Protein Contact Prediction

Proteins are the main machinery of life and protein functions are largely determined by their 3D structures. The measurement of the pairwise proximity between amino acids of a protein, known as inter-residue contact map, well characterizes the structural information of a protein. Protein contact prediction (PCP) is an essential building block of many protein structure related applications. The prevalent approach to contact prediction is based on estimating the inter-residue contacts using hand-crafted coevolutionary features derived from multiple sequence alignments (MSAs). To mitigate the information loss caused by hand-crafted features, some recently proposed methods try to learn residue co-evolutions directly from MSAs. These methods generally derive coevolutionary features by aggregating the learned residue representations from individual sequences with equal weights, which is inconsistent with the premise that residue co-evolutions are a reﬂection of collective covariation patterns of numerous homologous proteins. Moreover, non-homologous residues and gaps commonly exist in MSAs. By aggregating features from all homologs equally, the non-homologous information may cause misestimation of the residue co-evolutions. To overcome these issues, we propose an attention-based architecture, Co-evolution Transformer (CoT), for PCP. CoT jointly considers the information from all homologous sequences in the MSA to better capture global coevolutionary patterns. To mitigate the inﬂuence of the non-homologous information, CoT selectively aggregates the features from different homologs by assigning
Supplementary Material for “Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation”

The organization of the proofs is as follows. Frist, we show that the `1-type penalty has as special feature, allowing one to avoid estimating explicitly the estimation error. Second, a new element in the proof is proved in empirical process theory, in which we use the strong convexity of the expected functional objective under the Bernstein condition to enter directly into local conditions. Third, we use the intrinsic correlation between different RKHSs to derive a key inequality, which is related to the left side one of our basic inequality. Based on the above three steps, we can establish a basic polynomial-type inequality, which immediately yields our general learning rates for SVM.
Biotechnological interventions for crop improvement in the context of food security

Crop productivity is limited by a number of important constraints that need to be addressed urgently in order to avoid an imminent humanitarian crisis. My thesis provides three diverse yet converging examples of biotechnological solutions that can deliver fundamental knowledge, tools and potential products in the form of improved/enhanced crop plants. I conclude my thesis by discussing the potential of biotechnology to address the MDGs. My key conclusion is that although biotechnology can contribute positively and substantially towards many of the MDGs, political expediency and an over-burdening regulatory system threaten to prevent those needing the technology from gaining access, i.e. impoverished subsistence farmers and their families in the developing world.
Correlated Random-Effects Bivariate Poisson Lognormal Model to Study Single-Vehicle and Multivehicle Crashes

AbstractDeveloping crash-prediction models remains one of the primary approaches for studying traffic safety. Most of the current studies on single-vehicle (SV) and multivehicle (MV) crashes have only focused on the effects of exposure and geometric features of roadways and the effects of weather and traffic conditions are rarely incorporated. To provide more insightful observations, detailed weather and traffic data are adopted in this study. As a result of adopting detailed data, multiple daily observations are generated for SV and MV crashes on each roadway segment, forming a multivariate panel data set that poses some methodological challenges. A new approach to analyze SV and MV crashes is proposed by developing a bivariate Poisson lognormal model with correlated segment-specific random effects. The proposed model can characterize both the multivariate and panel nature of the data, and readily address the following three types of serial correlations within the multivariate panel data used in this stu.
OPDAylation of Thiols of the Redox Regulatory Network In Vitro

cis-(+)-12-Oxophytodienoic acid (OPDA) is a reactive oxylipin produced by catalytic oxygenation of polyunsaturated α-linolenic acid (18:3 (ω − 3)) in the chloroplast. Apart from its function as precursor for jasmonic acid synthesis, OPDA serves as a signaling molecule and regulator on its own, namely by tuning enzyme activities and altering expression of OPDA-responsive genes. A possible reaction mechanism is the covalent binding of OPDA to thiols via the addition to the C=C double bond of its α,β-unsaturated carbonyl group in the cyclopentenone ring. The reactivity allows for covalent modification of accessible cysteinyl thiols in proteins. This work investigated the reaction of OPDA with selected chloroplast and cytosolic thioredoxins (TRX) and glutaredoxins (GRX) of Arabidopsis thaliana. OPDA reacted with TRX and GRX as detected by decreased m-PEG maleimide binding, consumption of OPDA, reduced ability for insulin reduction and inability to activate glyceraldehyde-3-phosphate dehydrogenase and regenerate glutathione peroxidase (GPXL8), and with lower efficiency, peroxiredoxin IIB (PRXIIB). OPDAylation of certain protein thiols occurs quickly and efficiently in vitro and is a potent post-translational modification in a stressful environment.
The Impact of the COVID-19 Pandemic on Patients from a Bariatric Program: A Qualitative Analysis of Their Perceptions of Health and Well-Being

Introduction: The study was conducted to explore the perceptions of patients from a bariatric program who have undergone or will undergo bariatric surgery during the ongoing COVID-19 pandemic, specifically as related to their struggles with health issues and their psychological well-being. Materials and Methods: We conducted semi-structured, in-depth interviews with nineteen pre- or post-bariatric patients to generate data on their perceptions of COVID-19. Consistent with the methods of constructivist grounded theory, we collected and analyzed data iteratively through a constant comparative process for data coding and develop themes in the transcripts. Results: We identified themes to summarize the pandemic-associated experiences of our cohort as follows: their life structure before COVID-19, the turning point with changes and adaptations, and the impact of isolation on psychological well-being. We identified grief due to loss of social contacts as well as physical and psychological health impairment as consequences of pandemic-related lifestyle changes. Most participants were not aware of overweight and obesity being major risk factors for worse outcomes of COVID-19. We developed a theme-based theory on patients’ perceptions and fears regarding the pandemic as they live through phases of grief. Discussion: Most participants shared critical perceptions about their own somatic and psychological health. These findings may inform recommendations and strategies for both patients and healthcare professionals to manage the challenges potentially presented by this vulnerable patient group in the context of the COVID-19 pandemic.
Listeria monocytogenes Lymphocytes Responding to Infection with + Is a Signature of Peritoneal CD4 Simultaneous Th1-Type Cytokine Expression

The robust murine response to infection with Listeria monocytogenes makes an excellent model to study the functional development of immune cells. We investigated the cellular immune response to i.p. infection using intracellular cytokine staining to identify Ag-speciﬁc lymphocytes. CD4 (cid:1) peritoneal exudate cells obtained 10 days postinfection predominantly coexpressed TNF- (cid:2) , IFN- (cid:3) , and IL-2 after polyclonal or Ag stimulation. A population of cells simultaneously making TNF- (cid:2) and IFN- (cid:3) was also detected but at a lower frequency. By following the kinetics of the response to Listeria , we found that CD4 (cid:1) lymphocytes coexpressing TNF- (cid:2) and IFN- (cid:3) dominated on day 6 postinfection and then declined. From days 10–27, TNF- (cid:2) (cid:1) IFN- (cid:3) (cid:1) IL-2 (cid:1) (triple-positive) was the most prevalent cytokine phenotype, and the frequency steadily declined. These characteristic cytokine expression patterns were observed in both primary and secondary responses to Listeria infection and developed even when infection was terminated with antibiotic treatment. A cytokine-assisted immunization procedure resulted in both double- and triple-positive cells, but the clear predominance of triple-positive cells required Listeria infection. Triple-positive cells were preferentially noted in the peritoneal cavity tissue site; spleen cells displayed a predominant population of double-positive T cells (TNF- (cid:2) (cid:1) IFN- (cid:3) (cid:1) ). We speculate that the appearance of triple-positive cells represents a functionally signiﬁcant subset important in host defense at nonlymphoid tissue sites. The Journal of Immunology, 2005,
Running head: AGING AND NEURAL RESPONSES TO TEMPORAL MODULATIONS OF SOUND 1

27 Perception of speech requires sensitivity to features, such as amplitude and frequency modulations, that are 28 often temporally regular. Previous work suggests age-related changes in neural responses to temporally 29 regular features, but little work has focused on age differences for different types of modulations. We 30 recorded magnetoencephalography in younger (21–33 years) and older adults (53–73 years) to investigate 31 age differences in neural responses to slow (2-6 Hz sinusoidal and non-sinusoidal) modulations in amplitude, 32 frequency, or combined amplitude and frequency. Audiometric pure-tone average thresholds were elevated 33 in older compared to younger adults, indicating subclinical hearing impairment in the recruited older-adult 34 sample. Neural responses to sound onset (independent of temporal modulations) were increased in 35 magnitude in older compared to younger adults, suggesting hyperresponsivity and a loss of inhibition in the 36 aged auditory system. Analyses of neural activity to modulations revealed greater neural synchronization 37 with amplitude, frequency, and combined amplitude-frequency modulations for older compared to younger 38 adults. This potentiated response generalized across different degrees of temporal regularity (sinusoidal and 39 non-sinusoidal), although neural synchronization was generally lower for non-sinusoidal modulation. 40 Despite greater synchronization, sustained neural activity was reduced in older compared to younger adults 41 for sounds modulated both sinusoidally and non-sinusoidally in frequency. Our results suggest age 42 differences in the sensitivity of the auditory system to features present in speech and other natural sounds. 43
Moderating Effect of Changes in Perceived Social Support during Pregnancy on the Emotional Health of Mothers and Fathers and on Baby’s Anthropometric Parameters at Birth

(1) Background: this study is based on a model of how changes in protective factors may affect the emotional health of mothers and fathers and thus influence the development of the baby. Our research goal is to determine whether variations in perceived social support moderate levels of stress and depression during pregnancy and/or the effect of parents’ emotional health on the baby’s anthropometric parameters. (2) Methods: to achieve these aims, a longitudinal study was made of 132 couples and babies, who were evaluated at weeks 12 and 32 of gestation and at birth. Separate analyses were performed for the mothers and fathers, focused on the role of social support in moderating their levels of depression and stress during pregnancy, and the consequent impact on the baby. (3) Results: the results obtained show the moderating effects of changes in social support on maternal and paternal stress and depression. Reduced social support during pregnancy is associated with higher levels of stress and depression in both parents and with a high cephalisation index in their babies. (4) Conclusions: special attention should be paid to social support, which can have a strong impact on the evolution of emotional health during pregnancy and concomitantly on the development of the baby.
Effect of PCSK9 Inhibitors on Hemostasis in Patients with Isolated Hypercholesterolemia

Background: In addition to reducing plasma lipids, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors may produce numerous nonlipid-related pleiotropic effects. The purpose of this trial was to determine the efficacy of PCSK9 inhibitors alone in patients with isolated hypercholesterolemia. Methods: The trial enrolled 21 individuals with isolated hypercholesterolemia and atherosclerosis who received alirocumab for 90 days (150 mg every two weeks). Lipids, glucose homeostasis factors, and hemostatic markers were measured in the plasma at baseline and after treatment. Results: The PCSK9 inhibitor administered to these patients reduced plasma levels/activity of fibrinogen (from 3.6 ± 0.5 to 2.9 ± 0.4 g/L, p < 0.01), factor VII (from 143.8 ± 16.7 to 114.5 ± 14.1%, p < 0.01) and plasminogen activator inhibitor-1 (PAI-1) (from 74.9 ± 13.9 to 52.8 ± 9.1 ng/mL, p < 0.001) without a significant reduction in von Willebrand factor levels, and it tended to prolong the partial thromboplastin and prothrombin times. Conclusion: Our findings indicate that treatment with PCSK9 inhibitors has a multipotential effect on fibrinolysis and coagulation in patients with isolated hypercholesterolemia and that this medication may have some future benefits in patients who are statin-intolerant or contraindicated to statin use.
Detection of Dental Diseases through X-Ray Images Using Neural Search Architecture Network

An important aspect of the diagnosis procedure in daily clinical practice is the analysis of dental radiographs. This is because the dentist must interpret different types of problems related to teeth, including the tooth numbers and related diseases during the diagnostic process. For panoramic radiographs, this paper proposes a convolutional neural network (CNN) that can do multitask classification by classifying the X-ray images into three classes: cavity, filling, and implant. In this paper, convolutional neural networks are taken in the form of a NASNet model consisting of different numbers of max-pooling layers, dropout layers, and activation functions. Initially, the data will be augmented and preprocessed, and then, the construction of a multioutput model will be done. Finally, the model will compile and train the model; the evaluation parameters used for the analysis of the model are loss and the accuracy curves. The model has achieved an accuracy of greater than 96% such that it has outperformed other existing algorithms.