Miracle-dz/UniFAR-FTUs huggingface dataset:
A PHARMACOGNOSTIC REVIEW ON ARTEMISIA ABSINTHIUM

Artemisia, being the widely distributed genus of the plant family Asteraceae encompasses about 500 species. Among them, Artemisia absinthium (A. absinthium) is a well-known herb commonly known as Wormwood and Afsanteen. A. absinthium reported to possess several therapeutic benefits in fever, inflammation, epilepsy, gastric problems and urinary disorders. Review of literature included PubMed, Science Direct searches with ‘A. absinthium’ and ‘Wormwood’ as initial key words. The search was further refined by looking for terms such as ‘Constituents’ (or composition) and ‘Activity’ (or effect) within the results. The major bioactive constituents of Wormwood are mono and sesquiterpenes. The present review comprises up to date information of traditional uses, phytochemistry and pharmacology of A. absinthium. The A. absinthium is a rich source of chemically novel compounds and needs elaborate screening strategies to dwell into the pharmacological effects of its phytoconstituents at the molecular level. This review article provides preliminary information and gives a direction for the basic and clinical research on A. absinthium.
An Integrated Approach for Improving Brand Consistency of Web Content

A consumer-dependent (business-to-consumer) organization tends to present itself as possessing a set of human qualities, which is termed the brand personality of the company. The perception is impressed upon the consumer through the content, be it in the form of advertisement, blogs, or magazines, produced by the organization. A consistent brand will generate trust and retain customers over time as they develop an affinity toward regularity and common patterns. However, maintaining a consistent messaging tone for a brand has become more challenging with the virtual explosion in the amount of content that needs to be authored and pushed to the Internet to maintain an edge in the era of digital marketing. To understand the depth of the problem, we collect around 300K web page content from around 650 companies. We develop trait-specific classification models by considering the linguistic features of the content. The classifier automatically identifies the web articles that are not consistent with the mission and vision of a company and further helps us to discover the conditions under which the consistency cannot be maintained. To address the brand inconsistency issue, we then develop a sentence ranking system that outputs the top three sentences that need to be changed for making a web article more consistent with the company’s brand personality.
An investigation on the nonclassical and quantum phase properties of a family of engineered quantum states

The main focus of this thesis is to study the nonclassical and phase properties of a family of engineered quantum states, most of which show various nonclassical features. The beauty of these states is that these states can be used to establish quantum supremacy. Earlier, a considerable amount of works has been reported on various types of quantum states and their nonclassical properties. Here, complementing the earlier works, the effect of non-Gaussianity inducing operators on the nonclassical and phase properties of displaced Fock states have been studied. This thesis includes 6 chapters. In Chapter 1, motivation behind performing the present work is stated explicitly, also the basic concepts of quantum optics are discussed with a specific attention on the witnesses and measures of nonclassicality. In Chapter 2, nonclassical properties of photon added and subtracted displaced Fock states have been studied using various witnesses of lower- and higher-order nonclassicality which are introduced in Chapter 1. In Chapter 3, we have continued our investigation on photon added and subtracted displaced Fock states (and their limiting cases). In this chapter, quantum phase properties of these states are investigated from a number of perspectives, and it is shown that the quantum phase properties are dependent on the quantum state engineering operations performed. In Chapter 4, we have continued our investigation on the impact of non-Gaussianity inducing operators on the nonclassical and phase properties of the displaced Fock states. In Chapter 5, we have performed a comparison between to process that are used in quantum state engineering to induce nonclassical features. Finally, this thesis is concluded in Chapter 6, where we have summarized the findings of this thesis and have also described scope of the future works.
Strong Hα emission in the young planetary mass companion 2MASS J0249−0557 c

Aims. Our objective is the optical and near-infrared spectroscopic characterisation of 2MASS J0249−0557 c, a recently discovered young planetary mass companion to the β Pictoris (~25 Myr) member 2MASS J0249−0557. Methods. Using the Visible and Infrared Survey Telescope for Astronomy Hemisphere Survey and the Two Micron All Sky Survey (2MASS) data, we independently identified the companion 2MASS J0249−0557 c. We also obtained low-resolution optical spectroscopy of this object using the Optical System for Imaging and low-intermediate-Resolution Integrated Spectroscopy spectrograph at the Gran Telescopio Canarias, and near-infrared spectroscopy using the Son of Isaac spectrograph on the New Technology Telescope. Results. We classified 2MASS J0249−0557 c with a spectral type of L2.5 ± 0.5 in the optical and L3 ± 1 in the near-infrared. We identified several spectroscopic indicators of youth both in the optical and in the near-infrared that are compatible with the age of the β Pictoris moving group: strong absorption due to oxides, weak alkaline atomic lines, and a triangular shape of the H-band pseudo-continuum. We also detect a strong Hα emission, with a pseudo-equivalent width (pEW) of −90−40+20 Å, which seems persistent at timescales from several days to a few years. This indicates strong chromospheric activity or disk accretion. Although many M-type brown dwarfs have strong Hα emission, this target is one of the very few L-type planetary mass objects in which this strong Hα emission has been detected. Lithium absorption at 6708 Å is observed with pEW ≲5 Å. We also computed the binding energy of 2MASS J0249−0557 c and obtained an (absolute) upper limit of U = (−8.8 ± 4.4) × 1032 J. Conclusions. Similarly to other young brown dwarfs and isolated planetary mass objects, strong Hα emission due to accretion or chromospheric activity is also present in young planetary mass companions at ages of some dozen million years. We also found that 2MASS J0249−0557 c is one of the wide substellar companions with the lowest binding energy known to date.
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance

As deep reinforcement learning (DRL) has been recognized as an effective approach in quantitative finance, getting hands-on experiences is attractive to beginners. However, to train a practical DRL trading agent that decides where to trade, at what price, and what quantity involves error-prone and arduous development and debugging. In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies. Along with easily-reproducible tutorials, FinRL library allows users to streamline their own developments and to compare with existing schemes easily. Within FinRL, virtual environments are configured with stock market datasets, trading agents are trained with neural networks, and extensive backtesting is analyzed via trading performance. Moreover, it incorporates important trading constraints such as transaction cost, market liquidity and the investor's degree of risk-aversion. FinRL is featured with completeness, hands-on tutorial and reproducibility that favors beginners: (i) at multiple levels of time granularity, FinRL simulates trading environments across various stock markets, including NASDAQ-100, DJIA, SP (ii) organized in a layered architecture with modular structure, FinRL provides fine-tuned state-of-the-art DRL algorithms (DQN, DDPG, PPO, SAC, A2C, TD3, etc.), commonly-used reward functions and standard evaluation baselines to alleviate the debugging workloads and promote the reproducibility, and (iii) being highly extendable, FinRL reserves a complete set of user-import interfaces. Furthermore, we incorporated three application demonstrations, namely single stock trading, multiple stock trading, and portfolio allocation. The FinRL library will be available on Github at link this https URL.
β-Xylosidase and β-mannosidase in combination improved growth performance and altered microbial profiles in weanling pigs fed a corn-soybean meal-based diet

Objective In this study, two glycosidases (XMosidases), β-xylosidase and β-mannosidase, were investigated on their in vitro hydrolysis activities of feed and on the improvement of growth performance in vivo in weanling pigs. Methods Enzyme activities of XMosidases in vitro were evaluated in test tubes and simulation of gastric and small intestinal digestion, respectively, in the presence of NSPase. In vivo study was performed in 108 weaned piglets in a 28-d treatment. Pigs were allotted to one of three dietary treatments with six replicate pens in each treatment. The three treatment groups were as follows: i) Control (basal diet); ii) CE (basal diets+CE); iii) CE-Xmosidases (basal diets+ CE+β-xylosidase at 800 U/kg and β-mannosidase at 40 U/kg). CE was complex enzymes (amylase, protease, xylanase, and mannanase). Results In vitro XMosidases displayed significant activities on hydrolysis of corn and soybean meal in the presence of non-starch polysaccharide degrading enzymes (xylanase and β-mannanase). In vitro simulation of gastric and small intestinal digestion by XMosidases showed XMosidases achieved 67.89%±0.22% of dry matter digestibility and 63.12%±0.21% of energy digestibility at 40°C for 5 hrs. In weanling pigs, additional XMosidases to CE in feed improved average daily gain, feed conversion rate (p<0.05), and apparent total tract digestibility of crude protein (p = 0.01) and dry matter (p = 0.02). XMosidases also altered the gut bacterial diversity and composition by increasing the proportion of beneficial bacteria. Conclusion Addition of a complex enzyme supplementation (contained xylanase, β-mannanase, protease and amylase), XMosidases (β-xylosidase and β-mannosidase) can further improve the growth performance and nutrient digestion of young pigs.
On tuning deep learning models: a data mining perspective

Deep learning algorithms vary depending on the underlying connection mechanism of nodes of them. They have various hyperparameters that are either set via specific algorithms or randomly chosen. Meanwhile, hyperparameters of deep learning algorithms have the potential to help enhance the performance of the machine learning tasks. In this paper, a tuning guideline is provided for researchers who cope with issues originated from hyperparameters of deep learning models. To that end, four types of deep learning algorithms are investigated in terms of tuning and data mining perspective. Further, common search methods of hyperparameters are evaluated on four deep learning algorithms. Normalization helps increase the performance of classification, according to the results of this study. The number of features has not contributed to the decline in the accuracy of deep learning algorithms. Even though high sparsity results in low accuracy, a uniform distribution is much more crucial to reach reliable results in terms of data mining.
On the Dynamics of Training Attention Models

The attention mechanism has been widely used in deep neural networks as a model component. By now, it has become a critical building block in many state-of-the-art natural language models. Despite its great success established empirically, the working mechanism of attention has not been investigated at a sufficient theoretical depth to date. In this paper, we set up a simple text classification task and study the dynamics of training a simple attention-based classification model using gradient descent. In this setting, we show that, for the discriminative words that the model should attend to, a persisting identity exists relating its embedding and the inner product of its key and the query. This allows us to prove that training must converge to attending to the discriminative words when the attention output is classified by a linear classifier. Experiments are performed, which validates our theoretical analysis and provides further insights.
Time-Series Snapshot Network as A New Model for Role Recommendation in OSS

The last decade has witnessed the rapid growth of open source software~(OSS). Still, all contributors may find it difficult to assimilate into OSS community even they are enthusiastic to make contributions. We thus suggest that role recommendation may benefit both the users and developers, i.e., once we are able to make successful role recommendation for those in need, it may dramatically contribute to the productivity of developers and the enthusiasm of users, thus further boosting OSS projects' development. Motivated by this potential, we study the role recommendation from email data via network embedding methods. In this paper, we introduce time-series snapshot network~(TSSN) which is a mixture network to model the interactions among users and developers. Based on the established TSSN, we perform temporal biased walk~(TBW) to automatically capture both temporal and structural information of the email network, i.e., the behavioral similarity between individuals in the OSS email network. Experiments on ten Apache datasets demonstrate that the proposed TBW significantly outperforms a number of advanced random walk based embedding methods, leading to the state-of-the-art recommendation performance.
Reactive Dicarbonyl Scavenging Effectively Reduces MPO-Mediated Oxidation of HDL and Preserves HDL Atheroprotective Functions

High-density lipoprotein (HDL) is atheroprotective by mediating cholesterol efflux, anti-inflammatory, and anti-oxidation functions. Atheroprotective functions of HDL are related to the activity of HDL-associated enzymes such as paraoxonase 1 (PON1). We examined the impact of inhibition of myeloperoxidase (MPO)-mediated HDL oxidation by PON1 on HDL malondialdehyde (MDA) content and HDL function. In the presence of PON1, crosslinking of apoAI in response to MPO-mediated oxidation of HDL was abolished and MDA-HDL adduct levels were decreased. In addition, PON1 prevented the impaired cholesterol efflux capacity of MPO-oxidized HDL from Apoe-/- macrophages. Direct modification of HDL with MDA increased apoAI crosslinking and reduced the cholesterol efflux capacity in a dose dependent manner. In addition, MDA modification of HDL reduced its anti-inflammatory function compared to native HDL as the expression of IL-1β and IL6 increased by 3-(p<0.05) and 1.8-fold (p<0.05) in Apoe-/- macrophages in response to LPS. MDA-HDL also had impaired ability to increase PON1 activity. Importantly, HDL from subjects with familial hypercholesterolemia (FH-HDL) versus controls had increased MDA-apoAI adducts, and normalization of the PON1 activity to PON1 mass revealed a 24 % (p<0.05) decrease in specific activity indicating that PON1 activity is also impaired in FH. Consistent with the impaired PON1 activity and increased MDA-apoAI, FH-HDL induced a pro-inflammatory response in Apoe-/- macrophages compared to incubation with LPS alone. FH-HDL versus control HDL also had an impaired ability to promote cholesterol efflux from Apoe-/- macrophages. Interestingly, reactive dicarbonyl scavengers effectively abolished MPO-mediated apoAI crosslinking, MDA adduct formation, and improved cholesterol efflux capacity. Importantly, in vivo treatment of hypercholesterolemic mice with reactive dicarbonyl scavengers effectively reduced MDA-HDL adduct formation and increased PON1 activity and HDL cholesterol efflux capacity, supporting a therapeutic potential of reactive carbonyl scavenging in maintaining HDL function.
Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation

Segmentation of organs of interest in 3D medical images is necessary for accurate diagnosis and longitudinal studies. Though recent advances using deep learning have shown success for many segmentation tasks, large datasets are required for high performance and the annotation process is both time consuming and labor intensive. In this paper, we propose a 3D few shot segmentation framework for accurate organ segmentation using limited training samples of the target organ annotation. To achieve this, a U-Net like network is designed to predict segmentation by learning the relationship between 2D slices of support data and a query image, including a bidirectional gated recurrent unit (GRU) that learns consistency of encoded features between adjacent slices. Also, we introduce a transfer learning method to adapt the characteristics of the target image and organ by updating the model before testing with arbitrary support and query data sampled from the support data. We evaluate our proposed model using three 3D CT datasets with annotations of different organs. Our model yielded significantly improved performance over state-of-the-art few shot segmentation models and was comparable to a fully supervised model trained with more target training data.
Watch and Learn: Mapping Language and Noisy Real-world Videos with Self-supervision

In this paper, we teach machines to understand visuals and natural language by learning the mapping between sentences and noisy video snippets without explicit annotations. Firstly, we define a self-supervised learning framework that captures the cross-modal information. A novel adversarial learning module is then introduced to explicitly handle the noises in the natural videos, where the subtitle sentences are not guaranteed to be strongly corresponded to the video snippets. For training and evaluation, we contribute a new dataset `ApartmenTour' that contains a large number of online videos and subtitles. We carry out experiments on the bidirectional retrieval tasks between sentences and videos, and the results demonstrate that our proposed model achieves the state-of-the-art performance on both retrieval tasks and exceeds several strong baselines. The dataset will be released soon.
Data Representing Ground-Truth Explanations to Evaluate XAI Methods

Explainable artificial intelligence (XAI) methods are currently evaluated with approaches mostly originated in interpretable machine learning (IML) research that focus on understanding models such as comparison against existing attribution approaches, sensitivity analyses, gold set of features, axioms, or through demonstration of images. There are problems with these methods such as that they do not indicate where current XAI approaches fail to guide investigations towards consistent progress of the field. They do not measure accuracy in support of accountable decisions, and it is practically impossible to determine whether one XAI method is better than the other or what the weaknesses of existing models are, leaving researchers without guidance on which research questions will advance the field. Other fields usually utilize ground-truth data and create benchmarks. Data representing ground-truth explanations is not typically used in XAI or IML. One reason is that explanations are subjective, in the sense that an explanation that satisfies one user may not satisfy another. To overcome these problems, we propose to represent explanations with canonical equations that can be used to evaluate the accuracy of XAI methods. The contributions of this paper include a methodology to create synthetic data representing ground-truth explanations, three data sets, an evaluation of LIME using these data sets, and a preliminary analysis of the challenges and potential benefits in using these data to evaluate existing XAI approaches. Evaluation methods based on human-centric studies are outside the scope of this paper.
Universal Peaks Ratio in the Spectral Energy Density of Double Hump Blazars, Gamma-Ray Bursts, and Microquasars?

The peak frequencies of the two broad humps evident in the spectral energy density of blazars (SED) are time dependent and vary a lot between different blazars. However, their ratio in most blazars appears to be almost universal and equal to m e c 2/4(1 + z)ϵ p to a good approximation, where m e is the electron mass, ϵ p is the peak energy of the cosmic microwave background radiation, and z is the redshift of the blazar. We discuss a possible origin of such a universal ratio in blazars: gamma-ray bursts (GRBs). We point out a possible connection between the knee in the energy spectrum of cosmic-ray electrons and the maximal peak energies of the two broad humps in the SED of high energy peaked blazars and GRBs. We also point out that a universal peak ratio in double hump blazars that belong to different classes in the BL Lac sequence may simply reflect different viewing angles of otherwise similar blazars.
GRAPHSPY: Fused Program Semantic-Level Embedding via Graph Neural Networks for Dead Store Detection

Production software oftentimes suffers from the issue of performance inefficiencies caused by inappropriate use of data structures, programming abstractions, and conservative compiler optimizations. It is desirable to avoid unnecessary memory operations. However, existing works often use a whole-program fine-grained monitoring method with incredibly high overhead. To this end, we propose a learning-aided approach to identify unnecessary memory operations intelligently with low overhead. By applying several prevalent graph neural network models to extract program semantics with respect to program structure, execution order and dynamic states, we present a novel, hybrid program embedding approach so that to derive unnecessary memory operations through the embedding. We train our model with tens of thousands of samples acquired from a set of real-world benchmarks. Results show that our model achieves 90% of accuracy and incurs only around a half of time overhead of the state-of-art tool.
Creative Sketch Generation

Sketching or doodling is a popular creative activity that people engage in. However, most existing work in automatic sketch understanding or generation has focused on sketches that are quite mundane. In this work, we introduce two datasets of creative sketches -- Creative Birds and Creative Creatures -- containing 10k sketches each along with part annotations. We propose DoodlerGAN -- a part-based Generative Adversarial Network (GAN) -- to generate unseen compositions of novel part appearances. Quantitative evaluations as well as human studies demonstrate that sketches generated by our approach are more creative and of higher quality than existing approaches. In fact, in Creative Birds, subjects prefer sketches generated by DoodlerGAN over those drawn by humans! Our code can be found at this https URL and a demo can be found at this http URL.