Miracle-dz/UniFAR-FTUs huggingface dataset:
Filtrations on block subalgebras of reduced enveloping algebras

We study the interaction between the block decompositions of reduced enveloping algebras in positive characteristic, the Poincaré-Birkhoff-Witt (PBW) filtration, and the nilpotent cone. We provide two natural versions of the PBW filtration on the block subalgebra [Formula: see text] of the restricted universal enveloping algebra [Formula: see text] and show these are dual to each other. We also consider a shifted PBW filtration for which we relate the associated graded algebra to the algebra of functions on the Frobenius neighborhood of [Formula: see text] in the nilpotent cone and the coinvariants algebra corresponding to [Formula: see text]. In the case of [Formula: see text] in characteristic [Formula: see text] we determine the associated graded algebras of these filtrations on block subalgebras of [Formula: see text]. We also apply this to determine the structure of the adjoint representation of [Formula: see text].
Galaxy properties in the cosmic web of EAGLE simulation

We investigate the dependence of the galaxy properties on cosmic web environments using the most up-to-date hydrodynamic simulation: Evolution and Assembly of Galaxies and their Environments (EAGLE). The baryon fractions in haloes and the amplitudes of the galaxy luminosity function decrease going from knots to filaments to sheets to voids. Interestingly, the value of L$^*$ varies dramatically in different cosmic web environments. At z = 0, we find a characteristic halo mass of $10^{12} h^{-1}\rm M_{\odot}$, below which the stellar-to-halo mass ratio is higher in knots while above which it reverses. This particular halo mass corresponds to a characteristic stellar mass of $1.8\times 10^{10} h^{-1}\rm M_{\odot}$. Below the characteristic stellar mass central galaxies have redder colors, lower sSFRs and higher metallicities in knots than those in filaments, sheets and voids, while above this characteristic stellar mass, the cosmic web environmental dependences either reverse or vanish. Such dependences can be attributed to the fact that the active galaxy fraction decreases along voids, sheets, filaments and knots. The cosmic web dependences get weaker towards higher redshifts for most of the explored galaxy properties and scaling relations, except for the gas metallicity vs. stellar mass relation.
DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing

Inspired by the philosophy employed by human beings to determine whether a presented face example is genuine or not, i.e., to glance at the example globally first and then carefully observe the local regions to gain more discriminative information, for the face anti-spoofing problem, we propose a novel framework based on the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN). In particular, we model the behavior of exploring face-spoofing-related information from image sub-patches by leveraging deep reinforcement learning. We further introduce a recurrent mechanism to learn representations of local information sequentially from the explored sub-patches with an RNN. Finally, for the classification purpose, we fuse the local information with the global one, which can be learned from the original input image through a CNN. Moreover, we conduct extensive experiments, including ablation study and visualization analysis, to evaluate our proposed framework on various public databases. The experiment results show that our method can generally achieve state-of-the-art performance among all scenarios, demonstrating its effectiveness.
Deep learning in photoacoustic tomography: current approaches and future directions

Abstract. Biomedical photoacoustic tomography, which can provide high-resolution 3D soft tissue images based on optical absorption, has advanced to the stage at which translation from the laboratory to clinical settings is becoming possible. The need for rapid image formation and the practical restrictions on data acquisition that arise from the constraints of a clinical workflow are presenting new image reconstruction challenges. There are many classical approaches to image reconstruction, but ameliorating the effects of incomplete or imperfect data through the incorporation of accurate priors is challenging and leads to slow algorithms. Recently, the application of deep learning (DL), or deep neural networks, to this problem has received a great deal of attention. We review the literature on learned image reconstruction, summarizing the current trends and explain how these approaches fit within, and to some extent have arisen from, a framework that encompasses classical reconstruction methods. In particular, it shows how these techniques can be understood from a Bayesian perspective, providing useful insights. We also provide a concise tutorial demonstration of three prototypical approaches to learned image reconstruction. The code and data sets for these demonstrations are available to researchers. It is anticipated that it is in in vivo applications—where data may be sparse, fast imaging critical, and priors difficult to construct by hand—that DL will have the most impact. With this in mind, we conclude with some indications of possible future research directions.
Detectability of hierarchical communities in networks

We study the problem of recovering a planted hierarchy of partitions in a network. The detectability of a single planted partition has previously been analysed in detail and a phase transition has been identified below which the partition cannot be detected. Here we show that, in the hierarchical setting, there exist additional phases in which the presence of multiple consistent partitions can either help or hinder detection. Accordingly, the detectability limit for non-hierarchical partitions typically provides insufficient information about the detectability of the complete hierarchical structure, as we highlight with several constructive examples.
A Convolutional LSTM based Residual Network for Deepfake Video Detection

In recent years, deep learning-based video manipulation methods have become widely accessible to masses. With little to no effort, people can easily learn how to generate deepfake videos with only a few victims or target images. This creates a significant social problem for everyone whose photos are publicly available on the Internet, especially on social media websites. Several deep learning-based detection methods have been developed to identify these deepfakes. However, these methods lack generalizability, because they perform well only for a specific type of deepfake method. Therefore, those methods are not transferable to detect other deepfake methods. Also, they do not take advantage of the temporal information of the video. In this paper, we addressed these limitations. We developed a Convolutional LSTM based Residual Network (CLRNet), which takes a sequence of consecutive images as an input from a video to learn the temporal information that helps in detecting unnatural looking artifacts that are present between frames of deepfake videos. We also propose a transfer learning-based approach to generalize different deepfake methods. Through rigorous experimentations using the FaceForensics++ dataset, we showed that our method outperforms five of the previously proposed state-of-the-art deepfake detection methods by better generalizing at detecting different deepfake methods using the same model.
Multiport Rapid Charging Power Converter

Rapid charger is getting more and more important as the electric vehicle (EV) getting popular. The rapid charging technique plays an important part in the electric vehicle development. Multiport converter is used in the rapid charging technique to reduce the required current and also provides some other advantages. In this paper, the literature review form multiport converter to the rapid charger of electric vehicle is introduced. Some topologies of multiport converter are discussed in the literature review. Triple active bridge topology (TAB) is selected as it is useful and commonly used. The feasibility of using Sinusoidal Pulse Width Modulation (SPWM) to control the converter is also discussed in the simulation part. A simple hardware design and experiment is included at the end.
History and Biology of Transglutaminase 2: A Synopsis

Transglutaminase 2 (TG2) belongs to the family of transglutaminase, a large group of intracellular and extracellular enzymes that primarily catalyze the Ca-dependent posttranslational modification of proteins. Discovery of the first transglutaminase in the early 1920s, has subsequently lead to the identification of nine members of the enzyme family including TG2; the most abundant, most popular and most studied member of the transglutaminase enzyme family. The popularity of TG2 is due to its uniqueness amongst other members of the Transglutaminase (Tgase) family. Its difference from other Tgase family members is due to its specialized structural and biochemical activities; abundant tissue distribution and sub-cellular localization; and multi-functionality and physiology. The growing interests in TG2 and related research has resulted to an attendant mega-research output; and the need to produce a well-structured compilation of data on this popular enzyme has arisen. It is against this background, that we have compiled herein, a synopsis of available literature on TG2 history, structural and biochemical activities, tissue distribution, and physiology. This was done with the view to providing a compendium of background information that could be handy to researchers and new interest in the field of TG2 research.
Classification of the mechanisms of wave energy dissipation in the nonlinear oscillations of coated and uncoated bubbles

Acoustic waves are dissipated when they pass through bubbly media. Dissipation by bubbles takes place through thermal damping (Td), radiation damping (Rd) and damping due to the friction of the liquid (Ld) and friction of the coating (Cd). Knowledge of the contributions of the Td, Rd, Ld and Cd during nonlinear bubble oscillations will help in optimizing bubble and ultrasound exposure parameters for the relevant applications by maximizing a desirable parameter. In this work we investigate the mechanisms of dissipation in bubble oscillations and their contribution to the total damping (Wtotal) in various nonlinear regimes. By using bifurcation analysis, we have classified nonlinear dynamics of bubbles that are sonicated with their 3rd superharmonic (SuH) and 2nd SuH resonance frequency (fr), pressure dependent resonance frequency (PDfr), fr, subharmonic (SH) resonance (fsh=2fr), pressure dependent SH resonance (PDfsh) and 1/3 order SH resonance. The corresponding Td, Rd, Ld, Cd, Wtotal, scattering to dissipation ratio (STDR), maximum wall velocity and maximum back-scattered pressure from non-destructive oscillations of bubbles were calculated and analyzed using the bifurcation diagrams. We classified different regimes of dissipation and provided parameter regions in which a particular parameter of interest (e.g. Rd) can be enhanced. Afterwards enhanced bubble activity is linked to some relevant applications in ultrasound. This paper represents the first comprehensive analysis of the nonlinear oscillations regimes and the corresponding damping mechanisms.
Scikit-network: Graph Analysis in Python

Scikit-network is a Python package inspired by scikit-learn for the analysis of large graphs. Graphs are represented by their adjacency matrix in the sparse CSR format of SciPy. The package provides state-of-the-art algorithms for ranking, clustering, classifying, embedding and visualizing the nodes of a graph. High performance is achieved through a mix of fast matrix-vector products (using SciPy), compiled code (using Cython) and parallel processing. The package is distributed under the BSD license, with dependencies limited to NumPy and SciPy. It is compatible with Python 3.6 and newer. Source code, documentation and installation instructions are available online.
The Physical Parameters of Clumps Associated with Class I Methanol Masers

We present a study of the association between class I methanol masers and cold dust clumps from the APEX Telescope Large Area Survey of the Galaxy (ATLASGAL) survey. It was found that almost 100% of class I methanol masers are associated with objects listed in the ATLASGAL compact source catalog. We find a statistically significant difference in the flux density, luminosity, number and column density, and temperature distributions of ATLASGAL sources associated with 95/44 GHz methanol masers compared with those ATLASGAL sources devoid of 95 GHz methanol masers. The masers tend to arise in clumps with higher densities, luminosities, and temperatures compared with both the full sample of ATLASGAL clumps, as well as the sample of ATLASGAL sources that were cross-matched with positions previously searched for methanol masers but with no detections. Comparison between the peak position of ATLASGAL clumps and the interferometric positions of the associated class I and II methanol masers reveals that class I masers are generally located at larger physical distances from the peak submillimeter emission than class II masers. We conclude that the tight association between ATLASGAL sources and class I methanol masers may be used as a link toward understanding the conditions of the pumping of these masers and evolutionary stages at which they appear.
Cascaded Models for Better Fine-Grained Named Entity Recognition

Named Entity Recognition (NER) is an essential precursor task for many natural language applications, such as relation extraction or event extraction. Much of the NER research has been done on datasets with few classes of entity types (e.g. PER, LOC, ORG, MISC), but many real world applications (disaster relief, complex event extraction, law enforcement) can benefit from a larger NER typeset. More recently, datasets were created that have hundreds to thousands of types of entities, sparking new lines of research (Sekine, 2008;Ling and Weld, 2012; Gillick et al., 2014; Choiet al., 2018). In this paper we present a cascaded approach to labeling fine-grained NER, applying to a newly released fine-grained NER dataset that was used in the TAC KBP 2019 evaluation (Ji et al., 2019), inspired by the fact that training data is available for some of the coarse labels. Using a combination of transformer networks, we show that performance can be improved by about 20 F1 absolute, as compared with the straightforward model built on the full fine-grained types, and show that, surprisingly, using course-labeled data in three languages leads to an improvement in the English data.
DDRQA: Dynamic Document Reranking for Open-domain Multi-hop Question Answering

Open-domain multi-hop question answering (QA) requires to retrieve multiple supporting documents, some of which have little lexical overlap with the question and can only be located by iterative document retrieval. However, multi-step document retrieval often incurs more relevant but non-supporting documents, which dampens the downstream noise-sensitive reader module for answer extraction. To address this challenge, we propose Dynamic Document Reranking (DDR) to iteratively retrieve, rerank and filter documents, and adaptively determine when to stop the retrieval process. DDR employs an entity-linked document graph for multi-document interaction, which boosts up the retrieval performance. Experiments on HotpotQA full wiki setting show that our method achieves more than 7 points higher reranking performance over the previous best retrieval model, and also achieves state-of-the-art question answering performance on the official leaderboard.
GLUCOSE: GeneraLized and COntextualized Story Explanations

When humans read or listen, they make implicit commonsense inferences that frame their understanding of what happened and why. As a step toward AI systems that can build similar mental models, we introduce GLUCOSE, a large-scale dataset of implicit commonsense causal knowledge, encoded as causal mini-theories about the world, each grounded in a narrative context. To construct GLUCOSE, we drew on cognitive psychology to identify ten dimensions of causal explanation, focusing on events, states, motivations, and emotions. Each GLUCOSE entry includes a story-specific causal statement paired with an inference rule generalized from the statement. This paper details two concrete contributions: First, we present our platform for effectively crowdsourcing GLUCOSE data at scale, which uses semi-structured templates to elicit causal explanations. Using this platform, we collected 440K specific statements and general rules that capture implicit commonsense knowledge about everyday situations. Second, we show that existing knowledge resources and pretrained language models do not include or readily predict GLUCOSE's rich inferential content. However, when state-of-the-art neural models are trained on this knowledge, they can start to make commonsense inferences on unseen stories that match humans' mental models.
Origin of Atlantic salmon (Salmo salar) at sea in Icelandic waters

Kristinn Olafsson1,2*, Sigurdur M. Einarsson3, John Gilbey4, Christophe Pampoulie5, Gudmundur O. Hreggvidsson1,2, Sigridur Hjorleifsdottir6, and Sigurdur Gudjonsson3 Faculty of Life and Environmental Sciences, University of Iceland, Sturlugata 7, 101 Reykjavik, Iceland Matis Ltd, Vı́nlandsleið 12, 113 Reykjavı́k, Iceland Institute of Freshwater Fisheries, Árleyni 22, 112 Reykjavı́k, Iceland Freshwater Fisheries Laboratory, Faskally, Pitlochry PH16 4LB, UK Marine Research Institute, Skúlagata 4, 101 Reykjavı́k, Iceland ORF Genetics, Vı́kurhvarf 3, 203 Kópavogur, Iceland *Corresponding author: tel: + 354 4225080; fax: + 354 4225001; e-mail: kristinn@matis.is
Evaluating the Environmental Literacy of Tourists Visiting in Arid Regions (Case Study: Qom city)

Contemporary environmental crisis is the result of human misunderstanding or ignorance in the world. Having thought about various environmental crises that human beings have undergone, we understand that what exacerbates these crises is ecological illiteracy, which causes a lot of problems in a society. Therefore, reforming environmental crisis, according to the environmentalists, is dependent on reforming human instruction and changing the attitude, insight and knowledge of human about his own destiny and his environment. This study aims at acquainting with knowledge, attitude and environmental behaviors of tourists visiting the arid city of Qom. 386 people were selected as the sample of the study via Cochran formula, 200 questionnaires were distributed among domestic tourists, and 186 questionnaires among foreign tourists. The research utilized a researchermade questionnaire and face validity and Cronbach's alpha coefficient were used for confirming its validity and its reliability (0.762). SPSS software was used for descriptive analysis and one-sample t-test, and AMOS software was used for structural equation modeling. T-test results with a significant level less than 0.05 shows that tourists have environmental value, behavior, attitude and knowledge. Also, there is a significant correlation between these variables. The factor loadings are 0.96 between environmental knowledge and environmental attitudes of tourists, 0.65 between environmental knowledge and environmental values of tourists and 0.53 between environmental attitudes and environmental values of tourists, and reciprocal relationship is confirmed at the significance level of 0.000. It can be concluded that strengthening the environmental attitude of tourists will affect their environmental values. Strengthening educational systems and informing people can lead to favorable and sustainable behaviors in tourism communities.
Association of Vitamin D Status with Polycystic Ovary Syndrome in Relation to Insulin Resistance and Serum Total Testosterone

Background: Polycystic ovary syndrome (PCOS) is the most common endocrinopathy among women. Insulin resistance is now recognized as an integral component, particularly in obese. Association of vitamin D status with metabolic and hormonal dysfunction in PCOS has been investigated by some studies but the relationship between them still remains to be conclusive. The link between altered vitamin D status (if any) and androgen excess may have dependence on insulin resistance associated with PCOS. Aims and objectives: The present study aims to find out interrelationship between serum Vit.D, Total Testosterone levels and Insulin resistance in PCOS cases. Materials and methods: The study included 150 PCOS patients from Gynaecology and Endocrinology OPD of a tertiary care hospital of Kolkata, West Bengal, and 150 agematched healthy volunteers. Serum 25(OH) Vit D, total testosterone and insulin levels were estimated by Enzyme Linked Immunosorbent Assay (ELISA) method. Insulin resistance was calculated from fasting serum insulin and fasting plasma glucose level with HOMA-IR formula (Homeostasis Model Assessment of Insulin Resistance). Results: Our study revealed that serum vitamin D levels were significantly lower in PCOS cases in comparison to age matched control group. Significant negative correlation of vitamin D status was found with insulin resistance and androgen status in PCOS cases. Conclusion: Though this is a small study, it has provided some clue into pathophysiology of PCOS. The relations and associations achieved from the results of this study sustained this explanation and so our study hints that vitamin D may have a decisive role in PCOS and has a potential to emerge as an associated biochemical parameter of PCOS.
On The Basis Of AI Automatically Releases Signal and Control the Traffic

: almost all (metropolitan) cities together with the main ones, like Los Angeles, Beijing, New York, are struggling from heavy visitors congestion. Statistics exhibit that, in 2015, 43 cities in China are suffering an extended tour time of greater than 1.5 h each day for the duration of rush hours. In the meanwhile, traffic accidents are plaguing the financial development as well. In order to attain a higher performance of detection and consciousness of multi-vehicle aims in complex city environment, a step of detection algorithm based on the points of Histogram of Oriented Gradients (HOG). This algorithm makes full use of HOG characteristic blessings for target vehicles, i.e., the appropriate descriptive capability of HOG feature. With the ever-increasing demand in city mobility and current logistics sector, the vehicle population has been gradually growing over the past various decades. One herbal final result of the automobile populace boom is the make bigger in traffic congestion. In our proposed method, the machine is designed to control visitors sign timings based totally on the density of visitors on its corresponding road. Detecting traffic thru roadside CCTV cameras. It acts as multi-class classification which is recognizing traffic. The system realize site visitors match in real-time. We right here advise a density based traffic sign scheduling algorithm.
A Fusion Recognition Method Based on Multifeature Hidden Markov Model for Dynamic Hand Gesture

In this paper, a fusion method based on multiple features and hidden Markov model (HMM) is proposed for recognizing dynamic hand gestures corresponding to an operator's instructions in robot teleoperation. In the first place, a valid dynamic hand gesture from continuously obtained data according to the velocity of the moving hand needs to be separated. Secondly, a feature set is introduced for dynamic hand gesture expression, which includes four sorts of features: palm posture, bending angle, the opening angle of the fingers, and gesture trajectory. Finally, HMM classifiers based on these features are built, and a weighted calculation model fusing the probabilities of four sorts of features is presented. The proposed method is evaluated by recognizing dynamic hand gestures acquired by leap motion (LM), and it reaches recognition rates of about 90.63% for LM-Gesture3D dataset created by the paper and 93.3% for Letter-gesture dataset, respectively.