Miracle-dz/UniFAR-FTUs huggingface dataset:
Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification

Multi-label classification (MLC) is a prediction task where each sample can have more than one label. We propose a novel contrastive learning boosted multi-label prediction model based on a Gaussian mixture variational autoencoder (C-GMVAE), which learns a multimodal prior space and employs a contrastive loss. Many existing methods introduce extra complex neural modules like graph neural networks to capture the label correlations, in addition to the prediction modules. We find that by using contrastive learning in the supervised setting, we can exploit label information effectively in a data-driven manner, and learn meaningful feature and label embeddings which capture the label correlations and enhance the predictive power. Our method also adopts the idea of learning and aligning latent spaces for both features and labels. In contrast to previous works based on a unimodal prior, C-GMVAE imposes a Gaussian mixture structure on the latent space, to alleviate the posterior collapse and over-regularization issues. C-GMVAE outperforms existing methods on multiple public datasets and can often match other models' full performance with only 50% of the training data. Furthermore, we show that the learnt embeddings provide insights into the interpretation of label-label interactions.
Deep-learning-based carotid artery vessel wall segmentation in black-blood MRI using anatomical priors

Carotid artery vessel wall thickness measurement is an essential step in the monitoring of patients with atherosclerosis. This requires accurate segmentation of the vessel wall, i.e., the region between an artery’s lumen and outer wall, in black-blood magnetic resonance (MR) images. Commonly used convolutional neural networks (CNNs) for semantic segmentation are suboptimal for this task as their use does not guarantee a contiguous ring-shaped segmentation. Instead, in this work, we cast vessel wall segmentation as a multi-task regression problem in a polar coordinate system. For each carotid artery in each axial image slice, we aim to simultaneously find two non-intersecting nested contours that together delineate the vessel wall. CNNs applied to this problem enable an inductive bias that guarantees ring-shaped vessel walls. Moreover, we identify a problem-specific training data augmentation technique that substantially affects segmentation performance. We apply our method to segmentation of the internal and external carotid artery wall, and achieve top-ranking quantitative results in a public challenge, i.e., a median Dice similarity coefficient of 0.813 for the vessel wall and median Hausdorff distances of 0.552 mm and 0.776 mm for lumen and outer wall, respectively. Moreover, we show how the method improves over a conventional semantic segmentation approach. These results show that it is feasible to automatically obtain anatomically plausible segmentations of the carotid vessel wall with high accuracy.
Iterative Frame-Level Representation Learning And Classiﬁcation For Semi-Supervised Temporal Action Segmentation

Temporal action segmentation classiﬁes the action of each frame in (long) video sequences. Due to the high cost of frame-wise labeling, we propose the ﬁrst semi-supervised method for temporal action segmentation. Our method hinges on un-supervised representation learning, which, for temporal action segmentation, poses unique challenges. Actions in untrimmed videos vary in length and have unknown labels and start/end times. Ordering of actions across videos may also vary. We propose a novel way to learn frame-wise representations from temporal convolutional networks (TCNs) by clustering input features with added time-proximity condition and multi-resolution similarity . By merging representation learning with conventional supervised learning, we develop an “Iterative-Contrast-Classify (ICC)” semi-supervised learning scheme. With more labelled data, ICC progressively improves in performance; ICC semi-supervised learning, with 40% labelled videos, performs similar to fully-supervised counterparts. Our ICC improves MoF by { +1.8, +5.6, +2.5 } % on Breakfast, 50Salads and GTEA respectively for 100% labelled videos.
Perception and Attitude of Reddit Users Towards Use of Face-Masks in Controlling COVID-19

In the wake of the COVID-19 pandemic, World Health Organization (WHO) recommended the use of face-masks to combat the disease. A fraction of the population has been vocal about their disapproval, especially on social media. Motivated by the need to understand their opinions, an international online survey in English was shared to the community /r/SampleSize in Reddit. The results obtained were summarized;binomial and multinomial confidence intervals were constructed for variables of interest;Pearson's Chi-squared test was used to test whether relationship exists between two variables of interest. A second, smaller survey was conducted to cross-validate the results. The study demonstrates that despite the digital polarization of public opinion, opinions from both sides of the spectrum are not ubiquitous.
Output‐only structural identification in time domain: Numerical and experimental studies

By identifying changes in stiffness parameters, structural damage can be detected and monitored. Although considerable progress has been made in this research area, many challenges remain in achieving robust structural identification based on incomplete and noisy measurement signals. The identification task is made even more difficult if measurement of input force is to be eliminated. To this end, an output‐only structural identification strategy is proposed to identify unknown stiffness and damping parameters. A non‐classical approach based on genetic algorithms (GAs) is adopted. The proposed strategy makes use of the recently developed GA‐based method of search space reduction, which has shown to be able to accurately and reliably identify structural parameters from measured input and output signals. By modifying the numerical integration scheme, input can be computed as the parameter identification task is in progress, thereby eliminating the need to measure forces. Numerical and experimental results demonstrate the power of the strategy in accurate and efficient identification of structural parameters and damage using only incomplete acceleration measurements. Copyright © 2007 John Wiley & Sons, Ltd.
Controlling Conditional Language Models with Distributional Policy Gradients

Machine learning is shifting towards general-purpose pretrained generative models, trained in a self-supervised manner on large amounts of data, which can then be applied to solve a large number of tasks. However, due to their generic training methodology, these models often fail to meet some of the downstream requirements (e.g. hallucination in abstractive summarization or wrong format in automatic code generation). This raises an important question on how to adapt pre-trained generative models to a new task without destroying its capabilities. Recent work has suggested to solve this problem by representing task-speciﬁc requirements through energy-based models (EBMs) and approximating these EBMs using distributional policy gradients (DPG). Unfortunately, this approach is limited to unconditional distributions, represented by unconditional EBMs. In this paper, we extend this approach to conditional tasks by proposing Conditional DPG (CDPG). We evaluate CDPG on three different control objectives across two tasks: summarization with T5 and code generation with GPT-Neo. Our results show that ﬁne-tuning using CDPG robustly moves these pretrained models closer towards meeting control objectives and — in contrast with baseline approaches — does not result in catastrophic forgetting.
From helical to standard magnetorotational instability: Predictions for upcoming liquid sodium experiments

We conduct a linear analysis of axisymmetric magnetorotational instability (MRI) in a magnetized cylindrical Taylor-Couette (TC) flow for its standard version (SMRI) with a purely axial background magnetic field and two further types -- helically modified SMRI (H-SMRI) and helical MRI (HMRI) -- in the presence of combined axial and azimuthal magnetic fields. This study is intended as preparatory for upcoming large-scale liquid sodium MRI experiments planned within the DRESDYN project at Helmholtz-Zentrum Dresden-Rossendorf, so we explore these instability types for typical values of the main parameters: the magnetic Reynolds number, the Lundquist number and the ratio of the angular velocities of the cylinders, which are attainable in these experiments. In contrast to previous attempts at detecting MRI in the lab, our results demonstrate that SMRI and its helically modified version can in principle be detected in the DRESDYN-TC device for the range of the above parameters, including the astrophysically most important Keplerian rotation, despite the extremely small magnetic Prandtl number of liquid sodium. Since in the experiments we plan to approach (H-)SMRI from the previously studied HMRI regime, we characterise the continuous and monotonous transition between the both regimes. We show that H-SMRI, like HMRI, represents an overstability (travelling wave) with non-zero frequency linearly increasing with azimuthal field. Because of its relevance to finite size flow systems in experiments, we also analyse the absolute form of H-SMRI and compare its growth rate and onset criterion with the convective one.
CO2Sum: Contrastive Learning for Factual-Consistent Abstractive Summarization

Generating factual-consistent summaries is a challenging task for abstractive summarization. Previous works mainly encode factual information or perform post-correct/rank after decoding. In this paper, we provide a factual-consistent solution from the perspective of contrastive learning, which is a natural extension of previous works. We propose CO2Sum (Contrastive for Consistency), a contrastive learning scheme that can be easily applied on sequence-to-sequence models for factual-consistent abstractive summarization, proving that the model can be fact-aware without modifying the architecture. CO2Sum applies contrastive learning on the encoder, which can help the model be aware of the factual information contained in the input article, or performs contrastive learning on the decoder, which makes the model to generate factual-correct output summary. What's more, these two schemes are orthogonal and can be combined to further improve faithfulness. Comprehensive experiments on public benchmarks demonstrate that CO2Sum improves the faithfulness on large pre-trained language models and reaches competitive results compared to other strong factual-consistent summarization baselines.
Attaining scalable storage-expansion dualism for bioartificial tissues

The untenable dependence of cryopreservation on cytotoxic cryoprotectants has motivated the mining of biochemical libraries to identify molecular features marking cryoprotectants that may prevent ice crystal growth. It is hypothesized that such molecules may be useful across all temperatures eliminating cellular destruction due to equilibration cytotoxicity before and after cryopreservation. By probing the biochemical space using solvation-associated molecular topology and partition-distribution measures we developed an analytic formalism for ice crystal inhibition. By probing the union between a heat-shock protein cluster and an anti-freeze glycoprotein cluster, the model development process generated distinct regions of interest for anti-freeze glycoprotein and proved robust across different classes of proteins. These results confirm that there is a chemical space within which efficacious Ice Crystal Inhibitor molecular libraries can be constructed. These spaces contain latent projections drawn from solvent accessibility, hydrogen bonding capacity and molecular geometry. They also showed that molecular design can be a useful tool in the development of new-age cryoactive matrices to enable the smooth transition across culture, preservation and expansion. These chemical spaces provide low cytotoxicity since such amphipathic molecules occupy a continuum between solubizing and membrane stabilizing regions as shown by the free energy of translocation calculations. These biochemical design spaces are fundamentally the solution to efficient scale-up and deployment of cell therapies. Consequently, this article proposes the use of a molecular knowledge-mining approach in the development of a class of non-cytotoxic cryoprotective agents in the class of Ice Crystal Inhibitor compatible with continuous cryothermic and normothermic cell storage and expansion.
MTFNet: Mutual-Transformer Fusion Network for RGB-D Salient Object Detection

—Salient object detection (SOD) on RGB-D images is an active problem in computer vision. The main challenges for RGB-D SOD problem are how to 1) extract the accurate features for RGB and Depth image data with clutter background or poor image quality and 2) explore the complementary information between RGB and Depth image data. To address these challenges, we propose a novel Mutual-Transformer Fusion Network (MTFNet) for RGB-D SOD. MTFNet contains two main modules, i.e. , Focal Feature Extractor (FFE) and Mutual-Transformer Fusion (MTF). FFE aims to extract the more accurate CNN features for RGB and Depth images by introducing a novel pixel-level focal regularization to guide CNN feature extractor. MTF is designed to deeply exploit the multi-modal interaction between RGB and Depth images on both coarse and ﬁne scales. The main beneﬁt of MTF is that it conducts the learning of intra-modality and inter-modality simultaneously and thus can achieve the communication across different modalities more directly and sufﬁciently. Comprehensive experimental results on six public benchmarks demonstrate the superiority of our proposed MTFNet.
Chromo-electric screening length in 2+1 flavor QCD

We study Polyakov loop as well as correlators of real and imaginary parts of the Polyakov loop in 2+1 flavor QCD at finite temperature. We use hypercubic (HYP) smearing to improve the signal in the lattice calculations and to obtain reliable results for the correlators at large distances. From the large distance behavior of the correlators we estimate the chromo-electric screening length to be (0.38-44)/T. Furthermore, we show that the short distance distortions due to HYP smearing do not affect the physics of interest
A comparison of torque capabilities of axial flux and radial flux type of brushless DC (BLDC) drives for wide speed range applications

This paper aims to evaluate capabilities of axial flux and radial flux brushless DC (AF and RF-BLDC) drives for washing machine applications (1:30 speed range). For this purpose, torque and EMF equations of the two types of motor are obtained in terms of electrical and magnetic loading and motor dimensions. Then the drives are evaluated on criteria such as efficiency, torque per unit mass and volume, torque per ampere etc. The AF-BLDC is found to have superior torque/mass ratio, however, this motor requires operation at high frequencies. Also, attention must be paid to control strategy to assure an acceptable torque/Amp ratio.
Highly accelerated MR parametric mapping by undersampling the k-space and reducing the contrast number simultaneously with deep learning

Introduction. To propose a novel deep learning-based method called RG-Net (reconstruction and generation network) for highly accelerated MR parametric mapping by undersampling k-space and reducing the acquired contrast number simultaneously. Methods. The proposed framework consists of a reconstruction module and a generative module. The reconstruction module reconstructs MR images from the acquired few undersampled k-space data with the help of a data prior. The generative module then synthesizes the remaining multi-contrast images from the reconstructed images, where the exponential model is implicitly incorporated into the image generation through the supervision of fully sampled labels. The RG-Net was trained and tested on the T1ρ mapping data from 8 volunteers at net acceleration rates of 17, respectively. Regional T1ρ analysis for cartilage and the brain was performed to assess the performance of RG-Net. Results. RG-Net yields a high-quality T1ρ map at a high acceleration rate of 17. Compared with the competing methods that only undersample k-space, our framework achieves better performance in T1ρ value analysis. Conclusion. The proposed RG-Net can achieve a high acceleration rate while maintaining good reconstruction quality by undersampling k-space and reducing the contrast number simultaneously for fast MR parametric mapping. The generative module of our framework can also be used as an insertable module in other fast MR parametric mapping methods.
Identification and verification of seed development related miRNAs in kernel almond by small RNA sequencing and qPCR

Many studies have investigated the role of miRNAs on the yield of various plants, but so far, no report is available on the identification and role of miRNAs in fruit and seed development of almonds. In this study, preliminary analysis by high-throughput sequencing of short RNAs of kernels from the crosses between almond cultivars ‘Sefid’ × ‘Mamaee’ (with small and large kernels, respectively) and ‘Sefid’ × ‘P. orientalis’ (with small kernels) showed that the expressions of several miRNAs such as Pdu-miR395a-3p, Pdu-miR8123-5p, Pdu-miR482f, Pdu-miR6285, and Pdu-miR396a were significantly different. These miRNAs targeted genes encoding different proteins such as NYFB-3, SPX1, PGSIP3 (GUX2), GH3.9, and BEN1. The result of RT-qPCR revealed that the expression of these genes showed significant differences between the crosses and developmental stages of the seeds, suggesting that these genes might be involved in controlling kernel size because the presence of these miRNAs had a negative effect on their target genes. Pollen source can influence kernel size by affecting hormonal signaling and metabolic pathways through related miRNAs, a phenomenon known as xenia.
Correlates of perceived stress among community-dwelling older African Americans

Background The purpose of this study was to identify correlates of perceived stress among older African Americans. Methods and findings Guided by the National Institute on Aging’s (NIA) Health Disparities Research Framework, we grouped correlates into four levels–environmental, sociocultural, behavioral, and biological, and performed a cross-sectional analysis using ordinal logistic regression models. Participants included 722 African Americans [mean age = 73.61 years (SD = 6.33)] from the Minority Aging Research Study (MARS). Several protective correlates from environmental (e.g., larger life space), sociocultural (e.g., larger social network size), behavioral (e.g., more purpose in life), and biological (e.g., higher global cognition) levels were associated with a lower odds of having higher levels of perceived stress. Conclusions Perceived stress was associated with established and novel correlates from every level. Future research is needed to examine how changes in these correlates may impact perceived stress in older African Americans.
Interannual measures of nutritional stress during a marine heatwave (the Blob) differ between two North Pacific seabird species

Abstract ‘The Blob’, a mass of anomalously warm water in the Northeast Pacific Ocean peaking from 2014 to 2016, caused a decrease in primary productivity with cascading effects on the marine ecosystem. Among the more obvious manifestations of the event were seabird breeding failures and mass mortality events. Here, we used corticosterone in breast feathers (fCort), grown in the winter period during migration, as an indicator of nutritional stress to investigate the impact of the Blob on two sentinel Pacific auk species (family Alcidae). Feathers were collected from breeding females over 8 years from 2010 to 2017, encompassing the Blob period. Since Pacific auks replace body feathers at sea during the migratory period, measures of fCort provide an accumulated measure of nutritional stress or allostatic load during this time. Changes in diet were also measured using δ15N and δ13C values from feathers. Relative to years prior to the Blob, the primarily zooplanktivorous Cassin’s auklets (Ptychoramphus aleuticus) had elevated fCort in 2014–2017, which correlated with the occurrence of the Blob and a recovery period afterwards, with relatively stable feather isotope values. In contrast, generalist rhinoceros auklets (Cerorhinca monocerata) displayed stable fCort values across years and increased δ15N values during the Blob. As marine heatwaves increase in intensity and frequency due to climate change, this study provides insight into the variable response of Pacific auks to such phenomena and suggests a means for monitoring population-level responses to climatological variation.
Scaling of ductility and damage-based strength reduction factors for horizontal motions

The conventional approach of obtaining the inelastic response spectra for the aseismic design of structures involves the reduction of elastic spectra via response modification factors. A response modification factor is usually taken as a product of (i) strength factor, RS, (ii) ductility factor, Rμ, and (iii) redundancy factor, RR. Ductility factor, also known as strength reduction factor (SRF), is considered to primarily depend on the initial time period of the single-degree-of-freedom (SDOF) oscillator and the displacement ductility demand ratio for the ground motion. This study proposes a preliminary scaling model for estimating the SRFs of horizontal ground motions in terms of earthquake magnitude, strong motion duration and predominant period of the ground motion, geological site conditions, and ductility demand ratio, with a given level of confidence. The earlier models have not considered the simultaneous dependence of the SRFs on various governing parameters. Since the ductility demand ratio is not a complete measure of the cumulative damage in the structure during the earthquake-induced vibrations, the existing definition of the SRF is sought to be modified with the introduction of damage-based SRF (in place of ductility-based SRF). A parallel scaling model has been proposed for estimating the damage-based SRFs. This model considers damage and ductility supply ratio as parameters instead of ductility demand ratio. Through a parametric study on ductility-based SRFs, it has been shown that the hitherto assumed insensitivity of earthquake magnitude and strong motion duration may not be always justified and that the initial time period of the oscillator plays an important role in the dependence of SRF on these parameters. Further, the damage-based SRFs are found to show similar parametric dependence as observed in the case of the ductility-based SRFs. Copyright © 2000 John Wiley & Sons, Ltd.
Machine Learning for Air Transport Planning and Management

In this work we compare the performance of several machine learning algorithms applied to the problem of modelling air transport demand. Forecasting in the air transport industry is an essential part of planning and managing because of the economic and financial aspects of the industry. The traditional approach used in airline operations as specified by the International Civil Aviation Organization is the use of a multiple linear regression (MLR) model, utilizing cost variables and economic factors. Here, the performance of models utilizing an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), a genetic algorithm, a support vector machine, and a regression tree are compared to MLR. The ANN and ANFIS had the best performance in terms of the lowest mean squared error.