Miracle-dz/UniFAR-FTUs huggingface dataset:
Antidiabetic Effects of the Ethanolic Root Extract of Uvaria chamae P. Beauv (Annonaceae) in Alloxan-Induced Diabetic Rats: A Potential Alternative Treatment for Diabetes Mellitus

Diabetes mellitus has been a menace to mankind from time immemorial. However, a natural product such as U. chamae P. Beauv (Annonaceae) offers alternative treatment for diabetes mellitus. The study aimed at evaluating antidiabetic activity of the ethanolic root extract of U. chamae in alloxan-induced diabetic rats. Diabetes was induced in Sprague Dawley rats after overnight fast with 150 mg/kg alloxan intraperitoneally. After 72 h, those with plasma glucose levels >200 mg/dl were classified as diabetic. Five diabetic rats in each group were treated daily for 14 days orally with 100, 250, and 400 mg/kg of the extract, glibenclamide (71 µg/kg) and pioglitazone (429 µg/kg), respectively, while another group was untreated. Control received 0.5 ml of Acacia senegal. Effects of extract on glucose, other biochemical, and hematological parameters were evaluated. α-amylase and α-glucosidase inhibitory activities of extract and its fractions were also evaluated. Percentage inhibition and IC50 values were determined. Diabetic control was achieved on the 7th day of the study with 100, 250, and 400 mg/kg of the extract showing glucose reduction of 72.14%, 78.75%, and 87.71%, respectively. The HDL-cholesterol levels of diabetic rats treated with extracts were significantly increased. Extract and its fractions caused α-amylase and α-glucosidase inhibition. Histologically, pancreas of diabetic rats treated with extract showed regenerated islet cells which were not seen in rats treated with glibenclamide and pioglitazone. This study showed that U. chamae has antidiabetic activity which may be through α-amylase and α-glucosidase inhibition and regeneration of pancreatic beta cells. Also, it may reduce the risk of cardiovascular disease by increasing HDL-cholesterol levels.
The Modeling of SDL Aiming at Knowledge Acquisition in Automatic Driving

In this paper we proposed an ultimate theory to solve the multi-target control problem through its introduction to the machine learning framework in automatic driving, which explored the implementation of excellent drivers' knowledge acquisition. Nowadays there exist some core problems that have not been fully realized by the researchers in automatic driving, such as the optimal way to control the multi-target objective functions of energy saving, safe driving, headway distance control and comfort driving, as well as the resolvability of the networks that automatic driving relied on and the high-performance chips like GPU on the complex driving environments. According to these problems, we developed a new theory to map multitarget objective functions in different spaces into the same one and thus introduced a machine learning framework of SDL(Super Deep Learning) for optimal multi-targetcontrol based on knowledge acquisition. We will present in this paper the optimal multi-target control by combining the fuzzy relationship of each multi-target objective function and the implementation of excellent drivers' knowledge acquired by machine learning. Theoretically, the impact of this method will exceed that of the fuzzy control method used in automatic train.
Adversarial Defense of Image Classification Using a Variational Auto-Encoder

Deep neural networks are known to be vulnerable to adversarial attacks. This exposes them to potential exploits in security-sensitive applications and highlights their lack of robustness. This paper uses a variational auto-encoder (VAE) to defend against adversarial attacks for image classification tasks. This VAE defense has a few nice properties: (1) it is quite flexible and its use of randomness makes it harder to attack; (2) it can learn disentangled representations that prevent blurry reconstruction; and (3) a patch-wise VAE defense strategy is used that does not require retraining for different size images. For moderate to severe attacks, this system outperforms or closely matches the performance of JPEG compression, with the best quality parameter. It also has more flexibility and potential for improvement via training.
StoryGAN: A Sequential Conditional GAN for Story Visualization

In this work, we propose a new task called Story Visualization. Given a multi-sentence paragraph, the story is visualized by generating a sequence of images, one for each sentence. In contrast to video generation, story visualization focuses less on the continuity in generated images (frames), but more on the global consistency across dynamic scenes and characters -- a challenge that has not been addressed by any single-image or video generation methods. Therefore, we propose a new story-to-image-sequence generation model, StoryGAN, based on the sequential conditional GAN framework. Our model is unique in that it consists of a deep Context Encoder that dynamically tracks the story flow, and two discriminators at the story and image levels, to enhance the image quality and the consistency of the generated sequences. To evaluate the model, we modified existing datasets to create the CLEVR-SV and Pororo-SV datasets. Empirically, StoryGAN outperformed state-of-the-art models in image quality, contextual consistency metrics, and human evaluation.
Brain Network Analysis and Classification Based on Convolutional Neural Network

Background: Convolution neural networks (CNN) is increasingly used in computer science and finds more and more applications in different fields. However, analyzing brain network with CNN is not trivial, due to the non-Euclidean characteristics of brain network built by graph theory. Method: To address this problem, we used a famous algorithm “word2vec” from the field of natural language processing (NLP), to represent the vertexes of graph in the node embedding space, and transform the brain network into images, which can bridge the gap between brain network and CNN. Using this model, we analyze and classify the brain network from Magnetoencephalography (MEG) data into two categories: normal controls and patients with migraine. Results: In the experiments, we applied our method on the clinical MEG dataset, and got the mean classification accuracy rate 81.25%. Conclusions: These results indicate that our method can feasibly analyze and classify the brain network, and all the abundant resources of CNN can be used on the analysis of brain network.
Towards economic NMPC for multi-stage AC optimal power flow

Recently there has been considerable progress on the analysis of stability and performance properties of so-called economic Nonlinear Model Predictive Control (NMPC) schemes; i.e. NMPC schemes employing stage costs that are not directly related to distance measures of pre-computed setpoints. At the same time, with respect to the energy transition, the use of NMPC schemes is proposed and investigated in a plethora of papers in different contexts. For example receding-horizon approaches to generator dispatch problems, which is also known as multi-stage Optimal Power Flow (OPF), naturally lead to economic NMPC schemes based on non-convex discrete-time Optimal Control Problems (OCP). The present paper investigates the transfer of analytic results available for general economic NMPC schemes to receding-horizon multistage OPF. We propose a blueprint formulation of multi-stage opf including AC power flow equations. Based on this formulation we present results on the dissipativity and recursive feasibility properties of the underlying OCP. Finally, we draw upon simulations using a 5 bus system and a 118 bus system to illustrate our findings.
Variational Saccading: Efficient Inference for Large Resolution Images

Image classification with deep neural networks is typically restricted to images of small dimensionality such as 224 x 244 in Resnet models [24]. This limitation excludes the 4000 x 3000 dimensional images that are taken by modern smartphone cameras and smart devices. In this work, we aim to mitigate the prohibitive inferential and memory costs of operating in such large dimensional spaces. To sample from the high-resolution original input distribution, we propose using a smaller proxy distribution to learn the co-ordinates that correspond to regions of interest in the high-dimensional space. We introduce a new principled variational lower bound that captures the relationship of the proxy distribution's posterior and the original image's co-ordinate space in a way that maximizes the conditional classification likelihood. We empirically demonstrate on one synthetic benchmark and one real world large resolution DSLR camera image dataset that our method produces comparable results with ~10x faster inference and lower memory consumption than a model that utilizes the entire original input distribution. Finally, we experiment with a more complex setting using mini-maps from Starcraft II [56] to infer the number of characters in a complex 3d-rendered scene. Even in such complicated scenes our model provides strong localization: a feature missing from traditional classification models.
Loss-of-Function Alleles of Heading date 1 (Hd1) Are Associated With Adaptation of Temperate Japonica Rice Plants to the Tropical Region

Adaptation of temperate japonica rice varieties to tropical regions is impeded by extremely early flowering probably due to photoperiod change from long to short. However, constant breeding efforts led to development of temperate japonica varieties adapted to tropical/subtropical regions, but the genetic factor underlying this is still elusive. We analyzed the 45 diverse rice accessions and 12 tropical-adapted temperate japonica lines for the allele types of seven major flowering genes Hd1, OsPPR37, DTH8, Ghd7, Ehd1, RFT1, and Hd3a and flowering time under three different field conditions in temperate and tropical locations. The accessions originated from the tropical/subtropical regions preferred the non-functional alleles of Hd1 and not other flowering genes. The genetic effect analysis of each gene showed that only the functional Hd1 caused early flowering in the tropical location. All 12 temperate japonica breeding lines adapted to the tropics possessed the loss-of-function alleles of Hd1 with no change of other flowering genes compared to common Korean temperate japonica varieties. A phylogenetic analysis using 2,918 SNP data points revealed that the genome status of the 12 breeding lines were very similar to Korean temperate japonica varieties. These results indicate that the functional Hd1 alleles of temperate japonica varieties induced extremely early flowering in the tropics and the non-functional hd1 alleles brought about the adaptation of temperate japonica rice to tropical regions.
Magnesium-Sodium Hybrid Battery With High Voltage, Capacity and Cyclability

Rechargeable magnesium battery has been widely considered as a potential alternative to current Li-ion technology. However, the lack of appropriate cathode with high-energy density and good sustainability hinders the realization of competitive magnesium cells. Recently, a new concept of hybrid battery coupling metal magnesium anode with a cathode undergoing the electrochemical cycling of a secondary ion has received increased attention. Mg-Na hybrid battery, for example, utilizes the dendritic-free deposition of magnesium at the anode and fast Na+-intercalation at the cathode to reversibly store and harvest energy. In the current work, the principles that take the full advantage of metal Mg anode and Na-battery cathode to construct high-performance Mg-Na hybrid battery are described. By rationally applying such design principle, we constructed a Mg-NaCrO2 hybrid battery using metal Mg anode, NaCrO2 cathode and a mixture of all-phenyl complex (PhMgCl-AlCl3, Mg-APC) and sodium carba-closo-dodecaborate (NaCB11H12) as dual-salt electrolyte. The Mg-NaCrO2 cell delivered an energy density of 183 Wh kg−1 at the voltage of 2.3 V averaged in 50 cycles. We found that the amount of electrolyte can be reduced by using solid MgCl2 as additional magnesium reservoir while maintaining comparable electrochemical performance. A hypothetical MgCl2-NaCrO2 hybrid battery is therefore proposed with energy density estimated to be 215 Wh kg−1 and the output voltage over 2 V.
Sensorimotor Oscillations During a Reciprocal Touch Paradigm With a Human or Robot Partner

Robots provide an opportunity to extend research on the cognitive, perceptual, and neural processes involved in social interaction. This study examined how sensorimotor oscillatory electroencephalogram (EEG) activity can be influenced by the perceived nature of a task partner – human or robot – during a novel “reciprocal touch” paradigm. Twenty adult participants viewed a demonstration of a robot that could “feel” tactile stimulation through a haptic sensor on its hand and “see” changes in light through a photoreceptor at the level of the eyes; the robot responded to touch or changes in light by moving a contralateral digit. During EEG collection, participants engaged in a joint task that involved sending tactile stimulation to a partner (robot or human) and receiving tactile stimulation back. Tactile stimulation sent by the participant was initiated by a button press and was delivered 1500 ms later via an inflatable membrane on the hand of the human or on the haptic sensor of the robot partner. Stimulation to the participant’s finger (from the partner) was sent on a fixed schedule, regardless of partner type. We analyzed activity of the sensorimotor mu rhythm during anticipation of tactile stimulation to the right hand, comparing mu activity at central electrode sites when participants believed that tactile stimulation was initiated by a robot or a human, and to trials in which “nobody” received stimulation. There was a significant difference in contralateral mu rhythm activity between anticipating stimulation from a human partner and the “nobody” condition. This effect was less pronounced for anticipation of stimulation from the robot partner. Analyses also examined beta rhythm responses to the execution of the button press, comparing oscillatory activity when participants sent tactile stimulation to the robot or the human partner. The extent of beta rebound at frontocentral electrode sites following the button press differed between conditions, with a significantly larger increase in beta power when participants sent tactile stimulation to a robot partner compared to the human partner. This increase in beta power may reflect greater predictably in event outcomes. This new paradigm and the novel findings advance the neuroscientific study of human–robot interaction.
On Marginally Correct Approximations of Dempster-Shafer Belief Functions from Data

Mathematical Theory of Evidence (MTE), a foundation for reasoning under partial ignorance, is blamed to leave frequencies outside (or aside of) its framework. The seriousness of this accusation is obvious: no experiment may be run to compare the performance of MTE-based models of real world processes against real world data. In this paper we consider this problem from the point of view of conditioning in the MTE. We describe the class of belief functions for which marginal consistency with observed frequencies may be achieved and conditional belief functions are proper belief functions,%\ and deal with implications for (marginal) approximation of general belief functions by this class of belief functions and for inference models in MTE.
Research on the Security of Blockchain Data: A Survey

With the more and more extensive application of blockchain, blockchain security has been widely concerned by the society and deeply studied by scholars. Moreover, the security of blockchain data directly affects the security of various applications of blockchain. In this survey, we perform a comprehensive classification and summary of the security of blockchain data. First, we present classification of blockchain data attacks. Subsequently, we present the attacks and defenses of blockchain data in terms of privacy, availability, integrity and controllability. Data privacy attacks present data leakage or data obtained by attackers through analysis. Data availability attacks present abnormal or incorrect access to blockchain data. Data integrity attacks present blockchain data being tampered. Data controllability attacks present blockchain data accidentally manipulated by smart contract vulnerability. Finally, we present several important open research directions to identify follow-up studies in this area.
Differential Expression of Prostaglandin I2 Synthase Associated with Arachidonic Acid Pathway in the Oral Squamous Cell Carcinoma

Introduction Differential expression of genes encoding cytochrome P450 (CYP) and other oxygenases enzymes involved in biotransformation mechanisms of endogenous and exogenous compounds can lead to oral tumor development. Objective We aimed to identify the expression profile of these genes, searching for susceptibility biomarkers in oral squamous cell carcinoma. Patients and Methods Sixteen oral squamous cell carcinoma samples were included in this study (eight tumor and eight adjacent non-tumor tissues). Gene expression quantification was performed using TaqMan Array Human CYP450 and other Oxygenases 96-well plate (Applied Biosystems) by real time qPCR. Protein quantification was performed by ELISA and IHC methods. Bioinformatics tools were used to find metabolic pathways related to the enzymes encoded by differentially expressed genes. Results. CYP27B1, CYP27A1, CYP2E1, CYP2R1, CYP2J2, CYP2U1, CYP4F12, CYP4X1, CYP4B1, PTGIS, ALOX12, and MAOB genes presented differential expression in the oral tumors. After correction by multiple tests, only the PTGIS (Prostaglandin I2 Synthase) gene presented significant differential expression (P < 0.05). The PTGIS gene and protein were reduced in oral tumors. Conclusion PTGIS presents downexpression in oral tumors. PTGIS play an important role in the arachidonic acid metabolism. Arachidonic acid and/or metabolites are derived from this pathway, which can influence the regulation of important physiological mechanisms in tumorigenesis process.
Proteolytic Surface-Shaving and Serotype-Dependent Expression of SPI-1 Invasion Proteins in Salmonella enterica Subspecies enterica

We performed proteolytic surface-shaving with trypsin on three strains/sevovars of Salmonella enterica enterica (SEE): Newport, Kentucky, and Thompson. Surfaced-exposed proteins of live bacterial cells were digested for 15 min. A separate 20 h re-digestion was also performed on the supernatant of each shaving experiment to more completely digest protein fragments into detectable peptides for proteomic analysis by nano-liquid chromatography-electrospray ionization-Orbitrap mass spectrometry. Control samples (i.e., no trypsin during surface-shaving step) were also performed in parallel. We detected peptides of flagella proteins: FliC (filament), FliD (cap), and FlgL (hook-filament junction) as well as peptides of FlgM (anti-σ28 factor), i.e., the negative regulator of flagella synthesis. For SEE Newport and Thompson, we detected Salmonella pathogenicity island 1 (SPI-1) secreted effector/invasion proteins: SipA, SipB, SipC, and SipD, whereas no Sip proteins were detected in control samples. No Sip proteins were detected for SEE Kentucky (or its control) although sip genes were confirmed to be present. Our results may suggest a biological response (<15 min) to proteolysis of live cells for these SEE strains and, in the case of Newport and Thompson, a possible invasion response.
Catalog of quasars from the Kilo-Degree Survey Data Release 3

We present a catalog of quasars selected from broad-band photometric ugri data of the Kilo-Degree Survey Data Release 3 (KiDS DR3). The QSOs are identified by the random forest (RF) supervised machine learning model, trained on Sloan Digital Sky Survey (SDSS) DR14 spectroscopic data. We first cleaned the input KiDS data of entries with excessively noisy, missing or otherwise problematic measurements. Applying a feature importance analysis, we then tune the algorithm and identify in the KiDS multiband catalog the 17 most useful features for the classification, namely magnitudes, colors, magnitude ratios, and the stellarity index. We used the t-SNE algorithm to map the multidimensional photometric data onto 2D planes and compare the coverage of the training and inference sets. We limited the inference set to r < 22 to avoid extrapolation beyond the feature space covered by training, as the SDSS spectroscopic sample is considerably shallower than KiDS. This gives 3.4 million objects in the final inference sample, from which the random forest identified 190 000 quasar candidates. Accuracy of 97% (percentage of correctly classified objects), purity of 91% (percentage of true quasars within the objects classified as such), and completeness of 87% (detection ratio of all true quasars), as derived from a test set extracted from SDSS and not used in the training, are confirmed by comparison with external spectroscopic and photometric QSO catalogs overlapping with the KiDS footprint. The robustness of our results is strengthened by number counts of the quasar candidates in the r band, as well as by their mid-infrared colors available from the Wide-field Infrared Survey Explorer (WISE). An analysis of parallaxes and proper motions of our QSO candidates found also in Gaia DR2 suggests that a probability cut of pQSO > 0.8 is optimal for purity, whereas pQSO > 0.7 is preferable for better completeness. Our study presents the first comprehensive quasar selection from deep high-quality KiDS data and will serve as the basis for versatile studies of the QSO population detected by this survey.
Struggle for Existence: the models for Darwinian and non-Darwinian selection

Classical understanding of the outcome of the struggle for existence results in the Darwinian survival of the fittest. Here we show that the situation may be different, more complex and arguably more interesting. Specifically, we show that different versions of non-homogeneous logistic-like models with a distributed Malthusian parameter imply non-Darwinian survival of everybody. In contrast, the non-homogeneous logistic equation with distributed carrying capacity shows Darwinian survival of the fittest. We also consider an non-homogeneous birth-and-death equation and give a simple proof that this equation results in the survival of the fittest. In addition to this known result, we find an exact limit distribution of the parameters of this equation. We also consider frequency-dependent non-homogeneous models and show that although some of these models show Darwinian survival of the fittest, there is not enough time for selection of the fittest species. We discuss the well-known Gauze Competitive exclusion principle that states that Complete competitors cannot coexist. While this principle is often considered as a direct consequence of the Darwinian survival of the fittest, we show that from the point of view of developed mathematical theory complete competitors can in fact coexist indefinitely.
Survey: how much the academic startups know and use agile software and lean startup methodologies?

We analyzed, through a survey, the level of knowledge and of usage of the Agile Software methodologies and of the Lean Startup methodologies among the participants in an academic laboratory that aims to promote the best practices of entrepreneurial process and innovation between university students. This laboratory led to the creation of some startups based on the development of a software product and is a good experience and example of collaboration between industry and academia.