Miracle-dz/UniFAR-FTUs huggingface dataset:
Powerful protective effects of gallic acid and tea polyphenols on human hepatocytes injury induced by hydrogen peroxide or carbon tetrachloride in vitro

Gallic acid and tea polyphenols are found in tea and considered as natural antioxidants. In this study, the cytoprotective effects of gallic acid and tea polyphenols against human hepatocytes (HL - 7702 cell line) oxidative injury induced by hydrogen peroxide (H2O2) or carbon tetrachloride (CCl4) were evaluated in vitro, with -tocopherol as standard antioxidant. Some biochemical assays were carried out to determine the cytoprotective effects of gallic acid and tea polyphenols on the hepatocytes subjected to oxidative injury, including cell viability, the content of reduced glutathione in cells, lactate dehydrogenase leakage into culture medium and the formation of malondialdehyde in the cells. Statistical results showed that pretreatment of the cultured cells with gallic acid, tea polyphenols or tocopherol at concentrations of 5, 10, 20 mg/l for 30 min before oxidative injury of the cells could provide cytoprotective effect to the cells to improve cell viability, increase the content of reduced glutathione in cells, reduce lactate dehydrogenase leakage into culture medium and decrease the formation of malondialdehyde in the cells. It was shown that gallic acid and tea polyphenols displayed stronger cytoprotective effect against oxidative injury of the hepatocytes than -tocopherol at same addition level and gallic acid was the most powerful compound.
DEVELOPMENTAL CHANGE OF Ca2+-ATPASE ACTIVITY ON PLASMA MEMBRANE OF RAT PANCREAS DURING PERINATAL PERIOD

Cat+-ATPase activity in rat pancreatic exocrine cells and endocrine cells during perinatal development was cytochemically examined at the electron microscopic level. The intense enzyme activity on the plasma membrane of endocrine cells was observed from the beginning of differentiation of endocrine cells, as well as in adult rats. However, no activity was seen on the plasma membrane of exocrine cells in prenatal rats. Ca2+-ATPase activity on the basolateral plasma membrane of exocrine cells appeared after birth, whereas pancreatic acini and zymogen granules were fully developed during the late embryonic stage. It seemed that the appearance of Ca2+-ATPase activity on the plasma membrane corresponded to the beginning of exocrine secretory function. These results suggest that Ca2+-ATPase activity on plasma membrane of exocrine cells may be deeply involved in the secretory function and/or its regulation.
INTERACTIONS BETWEEN BIRDS AND FRUITS IN A NORTHERN FLORIDA HAMMOCK COMMUNITY

This paper examines interactions between 22 bird species and 45 species of bird-dispersed plants in a northern Florida hammock community from April 1982 to May 1984. Seasonal patterns of frugivore availability and fruiting phenology were determined and compared. Frugivore species diversity and abundance were highest during the fall and winter months and lowest in the spring and summer. Increases in frugivore numbers and diversity were attributed to the presence of migrant thrushes in the early fall and of over- wintering frugivores in the late fall and winter. The timing of fruiting for the plant species studied over an annual period showed a significant correlation with frugivore diversity and abundance. Spring and summer months showed the lowest number of fruiting species and fall and winter the highest, with a peak of 28 species in fruit in December. Fruiting patterns fell into four seasonal groups: summer, fall, fall-winter, and winter fruiting. Nine species showed summer fruiting, bearing ripe fruit between early spring and late summer. Summer-fruiting species were uncommon in the community and typically occurred in disturbed areas. These species appeared to use both mammals and birds for seed dispersal. The 12 fall-fruiting species ripened fruit at the peak of frugivore migration through northern Florida. Fall-fruiting species were common or abundant in the community and depended primarily on migrant thrushes for seed dispersal. The 20 fall-winter fruiting species produced highly persistent fruits that were available to migrant frugivores through fall and winter. The four winter-fruiting species matured fruit in December and depended on overwintering birds for seed dispersal. No clear seasonal trends were evident for ripening synchrony patterns. The bird-fruit dispersal system of the lower temperate latitude hammock community shows similarities in fruiting patterns to middle latitudes of North America. Mild winters, the presence of evergreen species, and large populations of overwintering frugivores, how- ever, have made winter fruiting more prevalent at lower latitudes than middle latitudes.
Crustal structure of the Aegean Sea and the Hellenides obtained from geophysical surveys

In the period 1971 to 1974 the territory of Greece has been geophysically investigated by Greek and German Institutions. Gravity and magnetic stations have been distributed at a spacing of one station per 25 km2. Five deep seismic sounding cross-sections have been fired and recorded along the lines : Jonian Sea Peloponnese, Amorgos Mikonos Evia, Crete : East-West, Cretean Sea : North-South, Cretean Sea : East-West. The results of the seimsic programms revealed a pure continental structure of variable thickness. The most attenuated area is that of the Cretean Sea, with only 22 km depth to the Moho-Discontinuity at the Bouguer gravity maximum of + 175 mgal. The very unevenly distributed sedimentary cover is composed of mainly Neogene Sediments with thickness of 3 3,5 km in local basins, JONGSMA et al., 1975. The Greek mainland, along the Pindos Chains has minimum Bouguer anomalies of120 to140 mgal and Moho-Depths" between 4246 km. The Aegean Area builds a large dome and incorporates also a large part of the Taurides, Western Turkey.
AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients

Most popular optimizers for deep learning can be broadly categorized as adaptive methods (e.g. Adam) and accelerated schemes (e.g. stochastic gradient descent (SGD) with momentum). For many models such as convolutional neural networks (CNNs), adaptive methods typically converge faster but generalize worse compared to SGD; for complex settings such as generative adversarial networks (GANs), adaptive methods are typically the default because of their stability.We propose AdaBelief to simultaneously achieve three goals: fast convergence as in adaptive methods, good generalization as in SGD, and training stability. The intuition for AdaBelief is to adapt the stepsize according to the "belief" in the current gradient direction. Viewing the exponential moving average (EMA) of the noisy gradient as the prediction of the gradient at the next time step, if the observed gradient greatly deviates from the prediction, we distrust the current observation and take a small step; if the observed gradient is close to the prediction, we trust it and take a large step. We validate AdaBelief in extensive experiments, showing that it outperforms other methods with fast convergence and high accuracy on image classification and language modeling. Specifically, on ImageNet, AdaBelief achieves comparable accuracy to SGD. Furthermore, in the training of a GAN on Cifar10, AdaBelief demonstrates high stability and improves the quality of generated samples compared to a well-tuned Adam optimizer. Code is available at this https URL
Adaptive Offline and Online Similarity-Based Caching

With similarity-based content delivery, the request for a content can be satisfied by delivering a related content under a dissimilarity cost. This letter addresses the joint optimization of caching and similarity-based delivery decisions across a network so as to minimize the weighted sum of average delay and dissimilarity cost. A convergent alternate gradient descent ascent algorithm is first introduced for an offline scenario with prior knowledge of the request rates, and then extended to an online setting. Numerical results validate the advantages of the approach with respect to standard per-cache solutions.
Absorption-line Environments of High-redshift BOSS Quasars

The early stage of massive galaxy evolution often involves outflows driven by a starburst or a central quasar plus cold mode accretion (infall), which adds to the mass build-up in the galaxies. To study the nature of these infall and outflows in the quasar environments, we have examined the correlation of narrow absorption lines (NALs) at positive and negative velocity shifts to other quasar properties, such as their broad absorption-line (BAL) outflows and radio-loudness, using spectral data from SDSS-BOSS DR12. Our results show that the incidence of associated absorption lines (AALs) and outflow AALs is strongly correlated with BALs, which indicates most AALs form in quasar-driven outflows. Multiple AALs are also strongly correlated with BALs, demonstrating quasar outflows tend to be highly structured and can create multiple gas components with different velocity shifts along our line of sight. Infall AALs appear less often in quasars with BALs than quasars without BALs. This suggests that BAL outflows act on large scale in host galaxies and inhibit the infall of gas from the IGM, supporting theoretical models in which quasar outflow plays an important role in the feedback to host galaxies. Despite having larger distances, infall AALs are more highly ionized than outflow AALs, which can be attributed to the lower densities in the infall absorbers.
Stellar initial mass function over a range of redshifts

The stellar Initial Mass Function (IMF) seems to be close to universal in the local star-forming regions. However, this quantity of a newborn stellar population responds differently at gas metallicities $Z \sim$ $Z_\odot$ than $Z$ = 0. A view on the cosmic star formation history suggests that the cooling agents in the gas vary both in their types and molecular abundances. For instance, in the primordial gas environment, the gas temperature can be higher by a factor of 30 as compared to the present day. Stellar radiation feedback and cosmic microwave background (CMB) radiation may even contribute towards increasing the floor temperature of the star-forming gas which subsequently can leave profound impacts on the IMF. We present the contribution of the radiation sources towards the thermodynamical evolution of the Jeans unstable gas cloud which experiences fragmentation and mass accretion. We find evidence which suggests that the latter becomes the dominant process for star formation efficiencies (SFE) above 5 - 7 %, thus increasing the average mass of the stars. We focus on the isolated and binary stellar configurations emerging during the gas collapse. The binary fraction on average remains 0.476 and contributes significantly towards the total SFE of 15 %.
Double Robust Representation Learning for Counterfactual Prediction

Causal inference, or counterfactual prediction, is central to decision making in healthcare, policy and social sciences. To de-bias causal estimators with high-dimensional data in observational studies, recent advances suggest the importance of combining machine learning models for both the propensity score and the outcome function. We propose a novel scalable method to learn double-robust representations for counterfactual predictions, leading to consistent causal estimation if the model for either the propensity score or the outcome, but not necessarily both, is correctly specified. Specifically, we use the entropy balancing method to learn the weights that minimize the Jensen-Shannon divergence of the representation between the treated and control groups, based on which we make robust and efficient counterfactual predictions for both individual and average treatment effects. We provide theoretical justifications for the proposed method. The algorithm shows competitive performance with the state-of-the-art on real world and synthetic data.
Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach

Fine-tuned pre-trained language models (LMs) have achieved enormous success in many natural language processing (NLP) tasks, but they still require excessive labeled data in the fine-tuning stage. We study the problem of fine-tuning pre-trained LMs using only weak supervision, without any labeled data. This problem is challenging because the high capacity of LMs makes them prone to overfitting the noisy labels generated by weak supervision. To address this problem, we develop a contrastive self-training framework, COSINE, to enable fine-tuning LMs with weak supervision. Underpinned by contrastive regularization and confidence-based reweighting, our framework gradually improves model fitting while effectively suppressing error propagation. Experiments on sequence, token, and sentence pair classification tasks show that our model outperforms the strongest baseline by large margins and achieves competitive performance with fully-supervised fine-tuning methods. Our implementation is available on https://github.com/yueyu1030/COSINE.
Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity

Probabilistic forecasting consists in predicting a distribution of possible future outcomes. In this paper, we address this problem for non-stationary time series, which is very challenging yet crucially important. We introduce the STRIPE model for representing structured diversity based on shape and time features, ensuring both probable predictions while being sharp and accurate. STRIPE is agnostic to the forecasting model, and we equip it with a diversification mechanism relying on determinantal point processes (DPP). We introduce two DPP kernels for modeling diverse trajectories in terms of shape and time, which are both differentiable and proved to be positive semi-definite. To have an explicit control on the diversity structure, we also design an iterative sampling mechanism to disentangle shape and time representations in the latent space. Experiments carried out on synthetic datasets show that STRIPE significantly outperforms baseline methods for representing diversity, while maintaining accuracy of the forecasting model. We also highlight the relevance of the iterative sampling scheme and the importance to use different criteria for measuring quality and diversity. Finally, experiments on real datasets illustrate that STRIPE is able to outperform state-of-the-art probabilistic forecasting approaches in the best sample prediction.
Diverse Keyphrase Generation with Neural Unlikelihood Training

In this paper, we study sequence-to-sequence (S2S) keyphrase generation models from the perspective of diversity. Recent advances in neural natural language generation have made possible remarkable progress on the task of keyphrase generation, demonstrated through improvements on quality metrics such as F1-score. However, the importance of diversity in keyphrase generation has been largely ignored. We first analyze the extent of information redundancy present in the outputs generated by a baseline model trained using maximum likelihood estimation (MLE). Our findings show that repetition of keyphrases is a major issue with MLE training. To alleviate this issue, we adopt neural unlikelihood (UL) objective for training the S2S model. Our version of UL training operates at (1) the target token level to discourage the generation of repeating tokens; (2) the copy token level to avoid copying repetitive tokens from the source text. Further, to encourage better model planning during the decoding process, we incorporate K-step ahead token prediction objective that computes both MLE and UL losses on future tokens as well. Through extensive experiments on datasets from three different domains we demonstrate that the proposed approach attains considerably large diversity gains, while maintaining competitive output quality.
Neural Deepfake Detection with Factual Structure of Text

Deepfake detection, the task of automatically discriminating machine-generated text, is increasingly critical with recent advances in natural language generative models. Existing approaches to deepfake detection typically represent documents with coarse-grained representations. However, they struggle to capture factual structures of documents, which is a discriminative factor between machine-generated and human-written text according to our statistical analysis. To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text. Our approach represents the factual structure of a given document as an entity graph, which is further utilized to learn sentence representations with a graph neural network. Sentence representations are then composed to a document representation for making predictions, where consistent relations between neighboring sentences are sequentially modeled. Results of experiments on two public deepfake datasets show that our approach significantly improves strong base models built with RoBERTa. Model analysis further indicates that our model can distinguish the difference in the factual structure between machine-generated text and human-written text.
Stellar velocity dispersion and dynamical mass of the ultra diffuse galaxy NGC 5846_UDG1 from the keck cosmic web imager

The ultra-diffuse galaxy in the NGC 5846 group (NGC 5846_UDG1) was shown to have a large number of globular cluster (GC) candidates from deep imaging as part of the VEGAS survey. Recently, Muller et al. published a velocity dispersion, based on a dozen of its GCs. Within their quoted uncertainties, the resulting dynamical mass allowed for either a dark matter free or a dark matter dominated galaxy. Here we present spectra from KCWI which reconfirms membership of the NGC 5846 group and reveals a stellar velocity dispersion for UDG1 of $\sigma_{GC}$ = 17 $\pm$ 2 km/s. Our dynamical mass, with a reduced uncertainty, indicates a very high contribution of dark matter within the effective radius. We also derive an enclosed mass from the locations and motions of the GCs using the Tracer Mass Estimator, finding a similar mass inferred from our stellar velocity dispersion. We find no evidence that the galaxy is rotating and is thus likely pressure-supported. The number of confirmed GCs, and the total number inferred for the system ($\sim$45), suggest a total halo mass of $\sim2 \times 10^{11}$ M$_{\odot}$. A cored mass profile is favoured when compared to our dynamical mass. Given its stellar mass of 1.1$\times$10$^{8}$ M$_{\odot}$, NGC 5846_UDG1 appears to be an ultra-diffuse galaxy with a dwarf-like stellar mass and an overly massive halo.
Expression of pathogenesis related proteins in black pepper ( Piper nigrum L.) in relation to Phytophthora foot rot disease

Foot rot caused by Phytophthora capsici is the most serious disease of black pepper. Though all varieties of black pepper are susceptible to this pathogen, variations do exist concerning the degree of tolerance and mechanisms of defense. The protein profiles of a relatively tolerant and a susceptible black pepper ( Piper nigrum ) variety along with that of a resistant wild species ( Piper colubrinum ) were evaluated to detect variations in the defense related proteins/enzyme expression in response to P. capsici infection. The SDS-PAGE analysis revealed two additional polypeptides of 16.5 and 8 kD in the leaves of the tolerant variety (‘Kalluvally’) on the second day after infection and these proteins expressed only on the fifth day in the susceptible ‘Panniyur-1’. Variety specific proteins of molecular weight 90 and 5.5 kD were also found expressed in ‘Panniyur-1’ while ‘Kalluvally’ had unique protein bands of 14.3, 8.8, and 7.0 kD. However, P. colubrinum with 16 distinct bands had an altogether different banding pattern. The native protein profile obtained also indicated the expression of two additional proteins in P. nigrum . The over-expressed protein was characterized as b-1,3 glucanase. The intensity of expression was directly related to the level of tolerance. The role of enzymes like phenylalanine ammonia lyase (PAL), chitinase, and peroxidase in defense mechanism was also analyzed. The resistant genotype P. colubrinum , possessed higher enzyme activities than the P. nigrum varieties tested. This study thus confirmed the role of b-1, 3 glucanase and related enzymes in the defense mechanism of black pepper against foot rot disease.
Effective of Different Carbon Concentration on Yield of Extracellular Polymeric Substances (EPS) Produced by Pseudomonas Putida ATCC 11172

The extracellular polymeric substances (EPS) extracted from Pseudomonas putida ATCC 11172 at different carbon concentration, where Luria broth used as medium growth with and without addition of 0.5% w/v (LBG 0.5%) and 1.0% w/v (LBG 1%) glucose. The Bound EPS were extracted by ethylene diamine tetra acetic acid (EDTA) methods and precipitated by cold ethanol 3:1; while free EPS extracted only by precipitate by cold ethanol 3:1. The study shows the clear effect of carbon concentration on the production of EPS. The amount of free EPS found 120.39±2.20, 111.16±1.75 and 100.38±2.0 mg g -1 dry cell for LB, LBG 0.5 and LBG 1 respectively. Where the yield of bound EPS shows fewer amounts from free EPS, the amount of bound EPS was 29.22±1.40, 21.18±0.90 and 17.10±1.10 mg g -1 dry cell for LB, LBG 0.5 and LBG 1 respectively.
Environmental conditions and human activity nexus. The case of Northern Italy during COVID-19 lockdown

During COVID-19, draconian countermeasures forbidding non-essential human activities have been adopted worldwide, providing an unprecedented setup for testing sustainability policies. We unravel causal relationships among 16 environmental conditions and human activity variables and argue that, despite a measurable decrease in NO2 concentration due to human activities, locking down a region is insufficient to significantly reduce emissions. Policy strategies more effective than lockdowns must be considered for pollution control and climate change mitigation.
Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit

Modern deep learning models have achieved great success in predictive accuracy for many data modalities. However, their application to many real-world tasks is restricted by poor uncertainty estimates, such as overconfidence on out-of-distribution (OOD) data and ungraceful failing under distributional shift. Previous benchmarks have found that ensembles of neural networks (NNs) are typically the best calibrated models on OOD data. Inspired by this, we leverage recent theoretical advances that characterize the function-space prior of an ensemble of infinitely-wide NNs as a Gaussian process, termed the neural network Gaussian process (NNGP). We use the NNGP with a softmax link function to build a probabilistic model for multi-class classification and marginalize over the latent Gaussian outputs to sample from the posterior. This gives us a better understanding of the implicit prior NNs place on function space and allows a direct comparison of the calibration of the NNGP and its finite-width analogue. We also examine the calibration of previous approaches to classification with the NNGP, which treat classification problems as regression to the one-hot labels. In this case the Bayesian posterior is exact, and we compare several heuristics to generate a categorical distribution over classes. We find these methods are well calibrated under distributional shift. Finally, we consider an infinite-width final layer in conjunction with a pre-trained embedding. This replicates the important practical use case of transfer learning and allows scaling to significantly larger datasets. As well as achieving competitive predictive accuracy, this approach is better calibrated than its finite width analogue.