Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Driveable Area for Autonomous Vehicles

Autonomous driving is a challenging problem where there is currently an intense focus on research and development. Human drivers are forced to make thousands of complex decisions in a short amount of time,quickly processing their surroundings and moving factors. One of these aspects, recognizing regions on the road that are driveable is vital to the success of any autonomous system. This problem can be addressed with deep learning framed as a region proposal problem. Utilizing a Mask R-CNN trained on the Berkeley Deep Drive (BDD100k) dataset, we aim to see if recognizing driveable areas, while also differentiating between the car's direct (current) lane and alternative lanes is feasible.
STUDIES ON IN VITRO ANTIOXIDANT ACTIVITY OF VARIOUS EXTRACTS OF BUCHANIA LANZAN LEAVES

Background: Breast cancer is the most prevalent disease among women around the world, and is also the leading cause of death from cancer. Herbal medicine is an actual prehistoric form of healthcare known to mankind. Herbal medicine is being used by people belonging to all the cultures. The present study is based on the antioxidant and anti-cancerous potential of Buchanania lanzan which is also named as Chironji. Buchanania lanzan (chironji) is a tree species which belongs to the family Anacardiaceae and is commercially very useful. Methods: In vitro Antioxidant Activity 2, 2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity and reducing power assay Result: The total antioxidant activity of Buchanania lanzanin leaves was assessed based on scavenging activity of DPPH free radicals, among them pet ether extract of leaves shows 25.54 % inhibition at conc. of 20 μg / ml and at 100 μg / ml it shows 38.95 % inhibition. Ethyl acetate extract at 20 μg/ml it shows 38.41% inhibition and at 100 μg/ml it shows 50.36% inhibition. In methanolic extract at 20 μg/ml it shows 34.42% inhibition and at 100 μg/ml it shows 57.43% inhibition. The IC50 value is defined as the concentration of the substrate that causes 50 % loss of the DPPH activity. Reducing power assay of Petroleum ether extracts, ethyl acetate extract and methanolic extract of Buchanania lanzan was studied at different concentrations and found to be increase with increase in concentration. Conclusion: Therefore, present study concludes that Buchanania lanzan is an excellent medicinal plant which holds numerous bioactive phytochemicals and it can be used in prevention of cancer.
Efficient Spatial Nearest Neighbor Queries Based on Multi-layer Voronoi Diagrams

Nearest neighbor (NN) problem is an important scientific problem. The NN query, to find the closest one to a given query point among a set of points, is widely used in applications such as density estimation, pattern classification, information retrieval and spatial analysis. A direct generalization of the NN query is the k nearest neighbors (kNN) query, where the k closest point are required to be found. Since NN and kNN problems were raised, many algorithms have been proposed to solve them. It has been indicated in literature that the only method to solve these problems exactly with sublinear time complexity, is to filter out the unnecessary spatial computation by using the pre-processing structure, commonly referred to as the spatial index. The recently proposed spatial indices available for NN search, are almost constructed through spatial partition. These indices are tree-like, and the tree-like hierarchical structure can usually significantly improve the efficiency of NN search. However, when the data are distributed extremely unevenly, it is difficult to satisfy both the balance of the tree and the non-overlap of the subspace corresponding to the nodes. Thus the acceleration performance of the tree-like indices is severely jeopardized. In this paper, we propose a non-tree spatial index which consists of multiple layers of Voronoi diagrams (MVD). This index can entirely avoid the dilemma tree-like structures face, and solve the NN problems stably with logarithmic time complexity. Furthermore, it is convenient to achieve kNN search by extending NN search on MVD. In the experiments, we evaluate the efficiency of this indexing for both NN search and kNN search by comparing with VoR-tree, R-tree and kd-tree. The experiments indicate that compared to NN search and kNN search with the other three indices, these two search methods have significantly higher efficiency with MVD.
Crowdsourcing a High-Quality Gold Standard for QA-SRL

Question-answer driven Semantic Role Labeling (QA-SRL) has been proposed as an attractive open and natural form of SRL, easily crowdsourceable for new corpora. Recently, a large-scale QA-SRL corpus and a trained parser were released, accompanied by a densely annotated dataset for evaluation. Trying to replicate the QA-SRL annotation and evaluation scheme for new texts, we observed that the resulting annotations were lacking in quality and coverage, particularly insufficient for creating gold standards for evaluation. In this paper, we present an improved QA-SRL annotation protocol, involving crowd-worker selection and training, followed by data consolidation. Applying this process, we release a new gold evaluation dataset for QA-SRL, yielding more consistent annotations and greater coverage. We believe that our new annotation protocol and gold standard will facilitate future replicable research of natural semantic annotations.
Hyper-SAGNN: a self-attention based graph neural network for hypergraphs

Graph representation learning for hypergraphs can be used to extract patterns among higher-order interactions that are critically important in many real world problems. Current approaches designed for hypergraphs, however, are unable to handle different types of hypergraphs and are typically not generic for various learning tasks. Indeed, models that can predict variable-sized heterogeneous hyperedges have not been available. Here we develop a new self-attention based graph neural network called Hyper-SAGNN applicable to homogeneous and heterogeneous hypergraphs with variable hyperedge sizes. We perform extensive evaluations on multiple datasets, including four benchmark network datasets and two single-cell Hi-C datasets in genomics. We demonstrate that Hyper-SAGNN significantly outperforms the state-of-the-art methods on traditional tasks while also achieving great performance on a new task called outsider identification. Hyper-SAGNN will be useful for graph representation learning to uncover complex higher-order interactions in different applications.
Title: Transdifferentiation of Human Umbilical Cord- Derived Mesenchymal Stem Cells in Dopaminergic Neurons in a Three-Dimensional Culture Running title: effect of matrigel on Differentiation of HUC-MSCs into dopaminergic neurons

1. Department of Anatomy, Faculty of Medicine, Ilam University of Medical Sciences, Ilam, Iran. 2. ENT and Head & Neck Research Center and Department, The five senses institute, Iran University of Medical Sciences, Tehran, Iran. 3. Department of Anatomy & Cell Biology, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran. 4. Immunogenetic Research Center, Department of Anatomy & Cell Biology, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran. 5. Research Laboratory for Embryology and Stem Cells, Department of Anatomical Sciences, School of Medicine, Ardabil University of Medical Sciences, Ardabil, Iran. 6. Deptartment of Anatomy, School of Medicine, Medical Sciences/University of Tehran, Tehran, Iran. 7. Anatomy Department, Faculty of Medicine, Qom University of Medical Sciences, Qom, Iran. 8. Department of Basic Sciences, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran. 9. Neuroscience Research Center, Department of Anatomy, Faculty of Medicine, Golestan University of Medical Sciences, Gorgan, Iran.
Modeling P waves in seismic noise correlations: Advancing fault monitoring using train traffic sources

The theory of Green’s function retrieval essentially requires homogeneously distributed noise sources. Even though these conditions are not fulfilled in nature, low-frequency (<1 Hz) surface waves generated by ocean-crust interactions have been used successfully to image the crust with unprecedented spatial resolution. In contrast to low-frequency surface waves, highfrequency (>1 Hz) body waves have a sharper, more localized sensitivity to velocity contrasts and temporal changes at depth. In general, their retrieval using seismic interferometry is challenging, and recent studies focus on powerful, localized noise sources. They have proven to be a promising alternative but break the assumptions of Green’s function retrieval. In this study, we present an approach to model correlations between P waves for these scenarios and analyze their sensitivity to 3D Earth structure. We perform a series of numerical experiments to advance our understanding of these signals and prepare for an application to fault monitoring. In the considered cases, the character of the signals strongly diverges from Green’s function retrieval, and the sensitivity to structure has significant contributions in the source direction. An accurate description of the underlying physics allows us to reproduce observations made in the context of monitoring the San Jacinto Fault in California using train-generated seismic waves. This approach provides new perspectives for detecting and localizing temporal velocity changes previously unnoticed by commonly exploited surface-wave reconstructions.
Study on Discovery Method of Cooperative Research Team Based on Improved Louvain Algorithm

According to the needs of scientific research project research and development, the research of cooperative team excavation methods was carried out. Aiming at the current difficulties in accurately and reliably defining and identifying cooperative research teams from co-author network, an improved Louvain algorithm that integrates core node recognition was proposed: Louvain-LSCR algorithm. Based on the analysis of the Louvain algorithm, considering the local topology of the node in the network and the communication range of the node, a new algorithm LSCR for core node identification was constructed. The LSCR algorithm and Louvain algorithm were merged to obtain a new and improved algorithm, Louvain-LSCR. In this algorithm, the leaf nodes in phase 1 of Louvain algorithm were first pruned to reduce calculations; then, seed nodes were selected according to the LSCR algorithm in phase 2. The experimental results on related datasets show that LSCR algorithm has certain advantages in identifying core nodes. The modularity of Louvian-LSCR algorithm is better than other algorithms, and the community structure is more reasonable. It was verified that the algorithm can mine potential cooperative research teams in co-author network.
Antioxidant Activity of Beer: An EPR Experiment for an Undergraduate Physical-Chemistry Laboratory

This laboratory experiment utilizes an exploration of the storage stability of beer to introduce basic aspects of electron-paramagnetic-resonance (EPR) spectroscopy. Radicals formed upon aerobic forced-aging of beer samples are established by using a spin-trap. The students are introduced to basic principles of EPR spectroscopy and food and radical chemistry with a simple example taken from everyday life. The methodology reported provides the students with valuable insights into EPR spectroscopy and the role of free radicals in food chemistry.
FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

The growing number of low-power smart devices in the Internet of Things is coupled with the concept of “edge computing” that is moving some of the intelligence, especially machine learning, toward the edge of the network. Enabling machine learning algorithms to run on resource-constrained hardware, typically on low-power smart devices, is challenging in terms of hardware (optimized and energy-efficient integrated circuits), algorithmic, and firmware implementations. This article presents a FANN-on-MCU, an open-source toolkit built upon the fast artificial neural network (FANN) library to run lightweight and energy-efficient neural networks on microcontrollers based on both the ARM Cortex-M series and the novel RISC-V-based parallel ultralow-power (PULP) platform. The toolkit takes multilayer perceptrons trained with FANN and generates code targeted to low-power microcontrollers. This article also presents detailed analyses of energy efficiency across the different cores, and the optimizations to handle different network sizes. Moreover, it provides a detailed analysis of parallel speedups and degradations due to parallelization overhead and memory transfers. Further evaluations include experimental results for three different applications using a self-sustainable wearable multisensor bracelet. The experimental results show a measured latency in the order of only a few microseconds and power consumption of a few milliwatts while keeping the memory requirements below the limitations of the targeted microcontrollers. In particular, the parallel implementation on the octa-core RISC-V platform reaches a speedup of $22\times $ and a 69% reduction in energy consumption with respect to a single-core implementation on Cortex-M4 for continuous real-time classification.
Evidentials as a mark of genre

This paper aims to study the different types and pragmatic functions of the Spanish evidential forms found in four discursive genres, in order to observe if any restrictions apply. All the evidentials are studied in a corpus containing 100,000 words, evenly distributed over colloquial conversations, press news, academic papers, and parliamentary debates. Specifically, together with the pragmatic functions of these evidentials, the four dimensions mode of knowing, type of source, accessibility, and degree of precision are analysed. The results reveal different tendencies in the use of evidentials, depending on the genre, and support the claim that the behaviour of evidentials is conditioned by the specific characteristics of each genre.
Trapping ultracold atoms in submicron period magnetic lattices

In recent years, ultracold atoms trapped in periodic lattices have attracted much attention as a simulator for condensed matter systems because of the ability to manipulate and precisely control the ultracold atoms. Periodic arrays of magnetic microtraps patterned on a magnetic film provide a potential complementary tool to conventional optical lattices for trapping arrays of ultracold atoms. Compared to optical lattices, magnetic lat- tices allow a higher degree of design flexibility by allowing almost arbitrary lattice geometries and they also allow lower technical noise and state-selective trapping of atoms. This paper reports the trapping of ultracold 87Rb atoms in 0.7 μm-period triangular and square magnetic lattices integrated on an atom chip as a step towards using magnetic lattices as a new platform for simulating condensed matter and quantum many-body phenomena in nontrivial lattice geometries.
Optimal Division of Molecules Into Training And Test Sets With A New Tool To Predict Pharmacophore In 3D-QSAR

According to the descriptors in the pharmacophore model, dividing molecules into training and test sets serves to create a good model. It is difficult to track the Local Reactive Descriptor (LRD) effect of the pharmacophore at each interaction point in the 3D metric system. A subset of clusters of atoms can correspond to all or part of the pharmacophore structure. In this study, the multidimensional system of the subset was reduced to a one-dimensional index and the Vector Fingerprint Functions (VFF) of the molecules were created. Models were established by dividing molecules with close and similar VFFs into training and test sets. Sub-clusters were examined for all molecules by applying the Genetic Algorithm (GA). The model was predicted using the Leave One Out-Cross Validation (LOO-CV) method and verified with an external test set. The statistical results of the model obtained according to the division in the new method we developed (Q2 = 0.604 and R2 = 0.760 for training-80 and external test-20 sets, respectively) were compared with random and manual division results.
Parameter Identification of the Two-Mass System with the help of Multi-layer Estimator

In the paper issues related to the identification of the basic parameters of the two-mass mechanical system are presented. After brief introduction the motivation of the work is introduced. The advanced control structures used in order to damp the torsional vibration are shortly described. Then the mathematical model of the drive is demonstrated. Next the extended Kalman filter is briefly described. Then the concept of multi-layer Kalman filter based estimator is presented. The proposed estimator is used to estimate the mechanical parameters of the drive. Then they are used in order to design the closed-loop control structure. The proposed solution is tested in simulation as well as experimental studies. The main concluding remarks are put at the end of the paper.
EEG characteristic analysis of coach bus drivers based on brain connectivity as revealed via a graph theoretical network

This study describes the detection of driving fatigue using the characteristics of brain networks in a real driving environment. First, the θ, β and 36–44 Hz rhythm from the EEG signals of drivers were extracted using wavelet packet decomposition (WPD). The correlation between EEG channels was calculated using a Pearson correlation coefficient and subsequently, the brain networks were built. Furthermore, the clustering coefficient (C) and global efficiency (G) of the complex brain networks were calculated to analyze the functional differences in the brains of drivers over time. Combined with the relative power spectrum ratio (β/θ) of EEG signals and the mean value from questionnaires, the correlation of data characteristics between brain networks and subjective and objective data was analyzed. The results show that changes in the fatigue state of drivers can be effectively detected by calculating the data characteristics of brain networks in a real driving environment.
Sub-Pilot-Scale Autocatalytic Pyrolysis of Wastewater Biosolids for Enhanced Energy Recovery

Improving onsite energy generation and recovering value-added products are common goals for sustainable used water reclamation. A new process called autocatalytic pyrolysis was developed at bench scale in our previous work by using biochar produced from the biosolids pyrolysis process itself as the catalyst to enhance energy recovery from wastewater biosolids. The large-scale investigation of this process was used to increase the technical readiness level. A sub-pilot-scale catalytic pyrolytic system was constructed for this scaled-up study. The effects of configuration changes in both pyrolytic and catalytic reactors were investigated as well as the effect of vapor-catalyst contact types (i.e., downstream, in-situ) on product yield and quality. The sub-pilot-scale test with downstream catalysis resulted in higher py-gas yields and lower bio-oil yields when compared to results from a previous batch, bench-scale process. In particular, the py-gas yields increased 2.5-fold and the energy contained in the py-gas approximately quadrupled compared to the control test without autocatalysis. Biochar addition to the feed biosolids before pyrolysis (in-situ catalysis) resulted in increased py-gas production, but the increase was limited. It was expected that using a higher input pyrolyzer with a better mixing condition would further improve the py-gas yield.
Effet anti-inflammatoire et anti obésité des extraits polyphénoliques de feuilles de caroube "Ceratonia siliqua" et cladode de figuier de barbarie "Opuntia ficus-indica"

Dans la presente etude, nous avons etudie les effets des infusions de feuilles de caroube et de cladodes OFI riches en polyphenols sur l'inflammation associee a l'obesite et la colite ulcereuse induite par le dextran de sulfate de sodium (DSS) chez les souris suisse.Des etudes in vitro ont revele que les extraits aqueux de feuilles de caroube et de cladodes OFI presentaient des proprietes anti-inflammatoires marquees par l'inhibition de la production d'IL-6, de TNF-α et d'oxyde nitrique (NO) dans les cellules RAW 264.7 stimulees par des lipopolysaccharides (LPS). Inhibition de la translocation du noyau NF-κβ. Pour des investigations in vivo, des souris mâles suisses ont ete soumises a un regime controle (ND) ou a un regime riche en graisses (HFD). A la 4eme semaine apres le debut de l'etude, les animaux ont recu ou non 1% d'infusion de feuilles de caroube ou d'OFI-cladode pendant 6 semaines et ont ete soumis a une administration de DSS a 2% dans l'eau potable au cours des 7 derniers jours. Apres sacrifice, les niveaux de cytokines pro-inflammatoires dans le plasma et l'expression de leur ARNm dans differents organes ont ete determines. Les resultats ont montre que les infusions de feuille de caroube et de OFI-cladode reduisaient la severite de l'inflammation associee a l'obesite induite par HFD et la colite aigue induite par le DSS indiquee par une diminution de l'expression des cytokines pro-inflammatoires (comme TNF-α, IL1b et IL-6) tissu adipeux et rate. En outre, les taux plasmatiques d'IL-6 et de TNF-α ont egalement ete reduits en reponse au traitement par les infusions. Ainsi, les infusions de feuilles de caroube et d'OFI-cladode ont empeche la permeabilite intestinale grâce a la restauration des proteines de jonctions serrees (Zo1, occludines) et a l'homeostasie immunitaire. Ainsi, l'effet anti-inflammatoire des feuilles de caroube et des cladodes OFI pourrait etre attribue a leurs polyphenols qui pourraient attenuer la gravite de l'inflammation associee a l'obesite et a la colite.
FTIR Spectroscopy of Natural Bio-Polymers Blends

Spectroscopy Department, National Research Centre, 12311 Dokki, Cairo, Egypt. ABSTRACT Blending natural polymers are considered as important approach towards the production of new biopolymers with unique properties. Blending could enhance surface reactivity and produce unique hydrogen bonding which could produce a new composite with new physical as well as chemical properties. Spectral recognition of such materials could be achieved with the help of molecular modeling techniques such as Fourier transform infrared spectroscopy FTIR. Accordingly this work is conducted to study the molecular structure of chitosan, starch, agarose, gelatin blends with different ratios. FTIR spectra virify the formation of blend which could be dedicated for many biological applications.
Matching Risks with Instruments in Development Banks

ABSTRACT This paper explores how development banks should deploy appropriate financial instruments to encourage real economic risk-taking while minimizing financial engineering risks. We distinguish real economic risks from financial engineering or intermediary risks and argue that using complex financial instruments to leverage additional private financing may undermine policy steer and lead to too much risk being taken by development banks. We then explore comparative advantages of different financial instruments such as loans, guarantees, equity, and insurance in tackling risks in normal times. Then we synthesize common features of development banks’ responses to the COVID-19 crisis. Finally, we propose future research directions.