Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of free-radical-scavenging and antibacterial activities, and brine shrimp toxicity of Scutellaria pinnatifida (Lamiaceae)

SUMMARY Scutellaria pinnatifida A. Hamilt. (Lamiaceae) is an endemic Turkish herb. This plant is also endemic to Iran, and grows abundantly in other central and western Asian countries. Several species of the Scutellaria are known for their traditional uses in the treatment of hypertension, arteriosclerosis, inflammatory diseases, hepatitis, allergy, cancer and diarrhoea. Free-radical-scavenging property, antibacterial activity and brine shrimp toxicity of the n-hexane, dichloromethane (DCM) and methanol (MeOH) extracts of S. pinnatifida were assessed using the 2,2-diphenyl-1-picryl-hydrazyl (DPPH) assay, the resazurin microtitre plate based assay, and the brine shrimp lethality assay, respectively. The DCM and MeOH extracts exhibited free-radical-scavenging property, with the RC values of 0.362 and 0.127 mg/ml, respectively. Among the solid-phase extraction fractions of the MeOH extract, the 50% aqueous-MeOH fraction showed the highest level of free-radicalscavenging activity (RC = 0.039 mg/ml). While the DCM extract showed low level of antibacterial activity against Bacillus subtilis and ampicillin-resistant Escherichia coli, the MeOH extract was active against B. cereus, B. subtilis, E. coli and ampicillin-resistant E. coli. However, the minimum inhibitory concentrations (MIC) of the MeOH extract against these bacterial strains were >10 mg/ml. None of the extracts showed any significant toxicity towards brine shrimps (LD = > 1.00 mg/ml).
Polymeric nanocomposite: Development, characterization, ex vivo and in vivo evaluation for ulcerative colitis

ABSTRACT Chitosan nanoparticles (CH-NPs) and chitosan-montmorillonite nanocomposites (CH-MMT-NCPs) were developed and characterized. The in vitro drug release data revealed that the CH-MMT-NCPs successfully retarded the drug release until it entered into the simulated colonic fluid. Experimental colitis was induced by trinitrobenzenesulfonic acid (TNBS) into albino rats and clinical activity score and histological evaluation revealed that the CH-MMT-NCPs markedly improved the healing efficacy as compare to CH-NPs and drug solution. The CH-MMT-NCPs showed remarkable reduction in the ulcer index (3.80 ± 0.54) as compared to the CH-NPs (7.03 ± 0.78) and the group received MSZ solution (9.85 ± 0.58), respectively. GRAPHICAL ABSTRACT
Paclitaxel modified Fe3O4 loaded albumin nanoparticles as drug delivery vehicles by self-assembly

In this paper, the preparation of Fe3O4/PTX/HSA nanoparticles with magnetic resonance imaging (MRI) and slow release functionality by a self-assembly method is presented. Firstly, hydrophobic Fe3O4 nanoparticles with an average size of 10 nm are synthesized by thermal decomposition of an iron–oleate complex and then modified and stabilized by hydrophobic paclitaxel (PTX). Lastly, Fe3O4/PTX nanoparticles with a 3 nm PTX shell on the Fe3O4 surface are loaded into human serum albumin (HSA) to form uniform Fe3O4/PTX/HSA nanoparticles by PTX–HSA interaction after breaking the disulfide bond and unfolding hydrophobic region of HSA. The novel PTX modified Fe3O4/PTX nanoparticles have good dispersity in ethanol and strong binding capacity with HSA, which can be homogenously dispersed in HSA matrices to form novel Fe3O4/PTX/HSA nanoparticles with a pie structure by the self-assembly method. The resulting Fe3O4/PTX/HSA nanoparticles with a high saturation magnetization value of 10.2 emu g−1, good T2 imaging functionality and excellent ability to cross the cell membrane have been demonstrated by magnetization curves, in vitro MRI and cellular uptake. Furthermore, the in vitro antitumor ability of the system has also been evaluated.
Impact of management style of managers on amount of parent participation in schools issues in the saveh city,Iran

This study aims to investigate the effect of the management style of the managers on the level of the parents' participation in the schools affairs. This study was conducted in 2013 and its statistical population included 312 subjects among all the teachers and parents of the public high schools of Iranian Saveh City (including 156 teachers and 156 parents who were the members of the Trainers-Parents Community). Among the mentioned population 144 subjects were selected as the research sample by usingCochranformula and sampling method of stratified random sampling method. In this process, 8 teacher and 8 parents were selected from each school in random. The data collection was conducted using two questionnaires: standard questionnaire of management styles, and a research-made questionnaire for the parents' participation. To analyze the data we relied on structural equation modeling method using LISREL 8.8 and Smart PLS 2 softwares in modeling and structural parts. In the modeling part we investigated the technical characteristics of the questionnaire and corrected the questionnaire as needed; and in the structural part, we used the structural coefficients of the model to test the hypotheses of the research. The results showed that the management style of the managers has a positive significant effect on the parents' participation in the school affairs and on the reinforcement of the relationship between the home and the school. This is while the management style of the managers has no significant effect on the educational affairs, supporting affairs, and the reinforcement of the relationship between the school and the society.
Deep Reinforcement Learning in Imperfect Information Games: No-limit Texas Hold’em Poker

Many real-world applications can be described as large-scale games of imperfect information. This particular set of problems is challenging due to the random factor that makes even adaptive methods fail to correctly model the problem and find the best solution. Neural Fictitious Self Play (NFSP) is a powerful algorithm for learning approximate Nash equilibrium of imperfect-information games from self-play and it showed real success in limit Texas Hold’em Poker. However, it uses only crude data as input and its core aspect is represented by a Deep Q-Network which is offline and is very hard or impossible to converge in more complex online games with large search scale and deep search depth. In this paper, I develop a new variant of NSFP that combines the established fictitious self-play with neural gradient play to improve the performance on large-scale zero-sum imperfect-information games. I use this algorithm to solve the more complex no-limit version of Texas Hold’em Poker using powerful handcrafted metrics and heuristics alongside crude, raw data. When applied to no-limit hold’em Poker, the agents trained through self-play outperformed the ones that used fictitious play with a normal-form single-step approach to the game. Moreover, I showed that my algorithm converges close to a Nash equilibrium within the limited training process of my agents with very limited hardware. Finally, my best self-play-based agent learnt a strategy that rivals expert human level.
Support Recovery in Universal One-bit Compressed Sensing

One-bit compressed sensing (1bCS) is an extreme-quantized signal acquisition method that has been widely studied in the past decade. In 1bCS, linear samples of a high dimensional signal are quantized to only one bit per sample (sign of the measurement). Assuming the original signal vector to be sparse, existing results either aim to find the support of the vector, or approximate the signal within an $\epsilon$-ball. The focus of this paper is support recovery, which often also computationally facilitates approximate signal recovery. A universal measurement matrix for 1bCS refers to one set of measurements that work for all sparse signals. With universality, it is known that $\tilde{\Theta}(k^2)$ 1bCS measurements are necessary and sufficient for support recovery (where $k$ denotes the sparsity). In this work, we show that it is possible to universally recover the support with a small number of false positives with $\tilde{O}(k^{3/2})$ measurements. If the dynamic range of the signal vector is known, then with a different technique, this result can be improved to only $\tilde{O}(k)$ measurements. Further results on support recovery are also provided.
Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review

The outbreak of novel coronavirus disease (COVID- 19) has claimed millions of lives and has affected all aspects of human life. This paper focuses on the application of deep learning (DL) models to medical imaging and drug discovery for managing COVID-19 disease. In this article, we detail various medical imaging-based studies such as X-rays and computed tomography (CT) images along with DL methods for classifying COVID-19 affected versus pneumonia. The applications of DL techniques to medical images are further described in terms of image localization, segmentation, registration, and classification leading to COVID-19 detection. The reviews of recent papers indicate that the highest classification accuracy of 99.80% is obtained when InstaCovNet-19 DL method is applied to an X-ray dataset of 361 COVID-19 patients, 362 pneumonia patients and 365 normal people. Furthermore, it can be seen that the best classification accuracy of 99.054% can be achieved when EDL_COVID DL method is applied to a CT image dataset of 7500 samples where COVID-19 patients, lung tumor patients and normal people are equal in number. Moreover, we illustrate the potential DL techniques in drug or vaccine discovery in combating the coronavirus. Finally, we address a number of problems, concerns and future research directions relevant to DL applications for COVID-19.
Improved strong lensing modelling of galaxy clusters using the Fundamental Plane: the case of Abell S1063

Aims. From accurate photometric and spectroscopic information, we build the Fundamental Plane (FP) relation for the early-type galaxies of the cluster Abell S1063. We use this relation to develop an improved strong lensing model of the cluster, and we decompose the cluster cumulative projected total mass profile into its stellar, hot gas and dark matter mass components. We compare our results with the predictions of cosmological simulations Methods. We calibrate the FP using Hubble Frontier Fields photometry, and data from the Multi Unit Spectroscopic Explorer on the Very Large Telescope. The FP allows us to determine the velocity dispersions of all 222 cluster members included in the model from their measured structural parameters. As for their truncation radii, we test a proportionality relation with the observed half-light radii. Fixing the mass contribution of the hot gas component from X-ray data, the mass density distributions of the diffuse dark matter haloes are optimised by comparing the observed and model-predicted positions of 55 multiple images of 20 background sources, distributed over the redshift range 0.73 − 6.11. We determine the uncertainties on the model parameters with Monte Carlo Markov chains. Results. We find that the most accurate predictions of the positions of the multiple images are obtained when the truncation radii of the member galaxies are approximately 2.3 times their effective radii. Compared to the previous work by Bergamini et al. (2019) on the same cluster, our model allows for the inclusion of some scatter on the relation between the total mass and the velocity dispersion of the cluster members. We notice a lower statistical uncertainty on the value of some model parameters. For instance, the main dark matter halo of the cluster has a core radius of 86 ± 2 kpc: the uncertainty on this value decreases by more than 30% with respect to the previous work. Taking advantage of a new estimate of the stellar mass of all cluster members from the HST multi-band data, we measure the cumulative two-dimensional mass profiles out to a radius of 350 kpc for all baryonic and dark matter components of the cluster. At the outermost radius of 350 kpc, we obtain a baryon fraction of 0.147 ± 0.002. We study the stellar-to-total mass fraction of the high-mass cluster members in our model, finding good agreement with the observations of wide galaxy surveys, and some disagreement with the predictions of halo occupation distribution studies based on N-body simulations. Finally, we compare the features of the sub-haloes as described by our model with those predicted by high-resolution hydrodynamical simulations. We obtain compatible results in terms of stellar over total mass fraction. On the other hand, we report some discrepancies both in terms of maximum circular velocity, which is an indication of the halo compactness, and of sub-halo total mass function in the central cluster
OnlineSTL: Scaling Time Series Decomposition by 100x

Decomposing a complex time series into trend, seasonality, and remainder components is an important primitive that facilitates time series anomaly detection, change point detection, and forecasting. Although numerous batch algorithms are known for time series decomposition, none operate well in an online scalable setting where high throughput and real-time response are paramount. In this paper, we propose OnlineSTL, a novel online algorithm for time series decomposition which is highly scalable and is deployed for real-time metrics monitoring on high-resolution, high-ingest rate data. Experiments on different synthetic and real world time series datasets demonstrate that OnlineSTL achieves orders of magnitude speedups (100x) for large seasonalities while maintaining quality of decomposition.
Searching for solar KDAR with DUNE

The observation of 236 MeV muon neutrinos from kaon-decay-at-rest (KDAR) originating in the core of the Sun would provide a unique signature of dark matter annihilation. Since excellent angle and energy reconstruction are necessary to detect this monoenergetic, directional neutrino flux, DUNE with its vast volume and reconstruction capabilities, is a promising candidate for a KDAR neutrino search. In this work, we evaluate the proposed KDAR neutrino search strategies by realistically modeling both neutrino-nucleus interactions and the response of DUNE. We find that, although reconstruction of the neutrino energy and direction is difficult with current techniques in the relevant energy range, the superb energy resolution, angular resolution, and particle identification offered by DUNE can still permit great signal/background discrimination. Moreover, there are non-standard scenarios in which searches at DUNE for KDAR in the Sun can probe dark matter interactions.
Predicting the 2020 US Presidential Election with Twitter

One major sub-domain in the subject of polling public opinion with social media data is electoral prediction. Electoral prediction utilizing social media data potentially would significantly affect campaign strategies, complementing traditional polling methods and providing cheaper polling in real-time. First, this paper explores past successful methods from research for analysis and prediction of the 2020 US Presidential Election using Twitter data. Then, this research proposes a new method for electoral prediction which combines sentiment, from NLP on the text of tweets, and structural data with aggregate polling, a time series analysis, and a special focus on Twitter users critical to the election. Though this method performed worse than its baseline of polling predictions, it is inconclusive whether this is an accurate method for predicting elections due to scarcity of data. More research and more data are needed to accurately measure this method’s overall effectiveness.
Best-of-All-Worlds Bounds for Online Learning with Feedback Graphs

We study the online learning with feedback graphs framework introduced by Mannor and Shamir (2011), in which the feedback received by the online learner is specified by a graph $G$ over the available actions. We develop an algorithm that simultaneously achieves regret bounds of the form: $\smash{\mathcal{O}(\sqrt{\theta(G) T})}$ with adversarial losses; $\mathcal{O}(\theta(G)\operatorname{polylog}{T})$ with stochastic losses; and $\mathcal{O}(\theta(G)\operatorname{polylog}{T} + \smash{\sqrt{\theta(G) C})}$ with stochastic losses subject to $C$ adversarial corruptions. Here, $\theta(G)$ is the clique covering number of the graph $G$. Our algorithm is an instantiation of Follow-the-Regularized-Leader with a novel regularization that can be seen as a product of a Tsallis entropy component (inspired by Zimmert and Seldin (2019)) and a Shannon entropy component (analyzed in the corrupted stochastic case by Amir et al. (2020)), thus subtly interpolating between the two forms of entropies. One of our key technical contributions is in establishing the convexity of this regularizer and controlling its inverse Hessian, despite its complex product structure.
A Markov Game Model for AI-based Cyber Security Attack Mitigation

The new generation of cyber threats leverages advanced AI-aided methods, which make them capable to launch multi-stage, dynamic, and effective attacks. Current cyber-defense systems encounter various challenges to defend against such new and emerging threats. Modeling AI-aided threats through game theory models can help the defender to select optimal strategies against the attacks and make wise decisions to mitigate the attack's impact. This paper first explores the current state-of-the-art in the new generation of threats in which AI techniques such as deep neural network is used for the attacker and discusses further challenges. We propose a Markovian dynamic game that can evaluate the efficiency of defensive methods against the AI-aided attacker under a cloud-based system in which the attacker utilizes an AI technique to launch an advanced attack by finding the shortest attack path. We use the CVSS metrics to quantify the values of this zero-sum game model for decision-making.
Sparse composite likelihood selection

Composite likelihood has shown promise in settings where the number of parameters $p$ is large due to its ability to break down complex models into simpler components, thus enabling inference even when the full likelihood is not tractable. Although there are a number of ways to formulate a valid composite likelihood in the finite-$p$ setting, there does not seem to exist agreement on how to construct composite likelihoods that are comp utationally efficient and statistically sound when $p$ is allowed to diverge. This article introduces a method to select sparse composite likelihoods by minimizing a criterion representing the statistical efficiency of the implied estimator plus an $L_1$-penalty discouraging the inclusion of too many sub-likelihood terms. Conditions under which consistent model selection occurs are studied. Examples illustrating the procedure are analysed in detail and applied to real data.
MUSE: Multi-Use Error Correcting Codes

In this work we present a new set of error correcting codes – Multi-Use Error Correcting Codes ( MUSE ECC ) – that have the ability to match reliability guarantees of all commodity, conventional state-of-the-art ECC with fewer bits of storage . MUSE ECC derives its power by building on arithmetic coding methods (ﬁrst used in an experimental system in 1960s). We show that our MUSE construction can be used as a “drop in” replacement within error correction frameworks used widely today. Further, we show how MUSE is a promising ﬁt for emerging technologies such as a DDR5 memories. Concretely, all instantiations of MUSE we show in this paper offer 100% Single Error Correction, and multi-bit error detection between 70% and 95% while using fewer check bits. MUSE ECC corrects failure of a single chip on a DIMM with check bit space savings of 12.5% compared to conventional techniques. The performance overheads, if any, are negligible. Our results open the possibility of reusing ECC storage for things beyond reliability without compromising reliability, thus solving a 40-year-old puzzle.
AGN jets and winds in polarized light: the case of Mrk 231

We present the results of a multifrequency, multiscale radio polarimetric study with the Very Large Array (VLA) of the Seyfert 1 galaxy and BALQSO, Mrk 231. We detect complex total and polarized intensity features in the source. Overall, the images indicate the presence of a broad, one-sided, curved outflow towards the south which consists of a weakly collimated jet with poloidal inferred magnetic fields, inside a broader magnetized ‘wind’ or ‘sheath’ component with toroidal inferred magnetic fields. The model of a kpc-scale weakly collimated jet/lobe in Mrk 231 is strengthened by its C-shaped morphology, steep spectral index throughout, complexities in the magnetic field structures, and the presence of self-similar structures observed on the 10-parsec-scale in the literature. The ‘wind’ may comprise both nuclear starburst (close to the core) and active galactic nuclei winds, where the latter maybe the primary contributor. Moving away from the core, the ‘wind’ component may also comprise the outer layers (or ‘sheath’) of a broadened jet. The inferred value of the (weakly collimated) jet production efficiency, ηjet ∼ 0.01, is consistent with the estimates in the literature. The composite jet and wind outflow in Mrk 231 appears to be low-power and matter-dominated, and oriented at a small angle to our line of sight.
Understanding Gender and Racial Disparities in Image Recognition Models

Large scale image classification models trained on top of popular datasets such as Imagenet have shown to have a distributional skew which leads to disparities in prediction accuracies across different subsections of population demographics. A lot of approaches have been made to solve for this distributional skew using methods that alter the model pre, post and during training. We investigate one such approach - which uses a multi-label softmax loss with cross-entropy as the loss function instead of a binary cross-entropy on a multi-label classification problem on the Inclusive Images dataset which is a subset of the OpenImages V6 dataset. We use the MR2 dataset, which contains images of people with self-identified gender and race attributes to evaluate the fairness in the model outcomes and try to interpret the mistakes by looking at model activations and suggest possible fixes.
FoleyGAN: Visually Guided Generative Adversarial Network-Based Synchronous Sound Generation in Silent Videos

Deep learning based visual-to-sound generation systems have been developed that identify and create audio features from video signals. However, these techniques often fail to consider the time-synchronicity of the visual and audio features. In this paper we introduce a novel method for guiding a class-conditioned GAN to synthesize representative audio with temporally-extracted visual information. We accomplish this visual-to-sound generation task by adapting the synchronicity traits between the audio-visual modalities. Our proposed FoleyGAN model is capable of conditioning action sequences of visual events leading to the generation of visually aligned realistic soundtracks. We expanded our previously proposed Automatic Foley data set. We evaluated FoleyGAN’s synthesized sound output through human surveys that show noteworthy (on average 81%) audio-visual synchronicity performance. Our approach outperforms other baseline models and audio-visual data sets in statistical and ablation experiments achieving improved IS, FID and NDB scores. In ablation analysis we showed the significance of our visual and temporal feature extraction method as well as augmented performance of our generation network. Overall, our FoleyGAN model showed sound retrieval accuracy of 76.08% surpassing existing visual-to-audio synthesis deep neural networks.