Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural equations of modeling in creating defensible spaces of residential complex, Case study: LALE & MILAD complexes in Ardabil province, Iran

The main purpose of the research was to study the role of defensible of residential complex spaces in Structural Equations of Modeling (SEM). Nowadays, increase in number of population and the genesis residential complexes in cities, the peoples from different races, income levels and education are forced together to living. In the meantime, access the largest source for crime prevention, done is defensible space design to strengthen the control of sense the environment in the people. The idea that was presented in 1972 by Oscar Newman, In addition was to being caused higher self esteem of low-income families, gave the opportunity that are important part of mainstream society.28 family (14 family from the LALE residential complex and 14 family from the MILAD residential complex)in Ardabil city were selected by random cluster Sampling. They were asked to their own beliefs in complexes included in the 26 questions. Validity of the instrument calculated by content validity and the understudying construct showed that the instrument had proper validity. Direct and indirect effects of variables on defensible of space complexes were calculated through path analysis and regression tests. The results of the present study with use the Lisrel 8.5 revealed that; Variables such as neighborhood relations, Soci-Economic Status (SES), and apartment rules have direct effect on defensible of residential complex spaces. In the above components, the apartment rules have significant effects on defensible of space complexes with regression coefficient=0.84( ! ) and neighborhood relations with Beta coefficient=0.06 have the lowest effect in among observed variables. Moreover, other variables of research consist of building height, total units with shared hallway and units density have inverse effects on defensible of space complexes that with increase in amount of them, is reduced degree of defend the space. Too, findings showed that factors such as education, income levels, age the resident people have direct effect on the defensible spaces of the residential complex.
Bootstrap ARDL Test on the Relationship among Trade, FDI and CO2 Emissions: Based on the Experience of BRICS Countries

We used the Bootstrap ARDL method to test the relationship between the export trades, FDI and CO2 emissions of the BRICS countries. We found that China's foreign direct investment and the lag one period of CO2 emissions have a cointegration on exports. South Africa's foreign direct investment and CO2 emissions have a cointegration relationship with the lag one period of exports, and South Africa's the lag one period of exports and foreign direct investment have a cointegration relationship with the lag one period of CO2 emissions. But whether it is China or South Africa, these three variables have no causal relationship in the long-term. Among the variables of other BRICS countries, Russia is the only country showed degenerate case #1 in McNown et al. mentioned in their paper. When we examined short-term causality, we found that CO2 emissions and export trade showed a reverse causal relationship, while FDI and carbon emissions were not so obvious. Export trade has a positive causal relationship with FDI. Those variables are different from different situations and different countries.
Verification and first test measurement of a microwave-based vital sign monitor

Abstract. The authors present a microwave-based vital sign monitoring system to detect the breathing rate of premature infants in a baby incubator. The sensor is a four channel I/Q-radar system with adapted antennas to cover the predefined region of interest on the patient surface of the incubator. In order to prove the correct illumination of the reclining area an electromotive actuator and a pneumatic dummy is used. With a periodic and repeatable breathing simulation the reflected signal in the sensor system is measured and evaluated. In the publication the radar system in the baby incubator, the electromotive actuator and the infant dummy are presented.
Chrysin regulates NAD(P)H:quinoneoxidoreductase 1 and inflammatory mediators in rats exposed to smoking and thioacetamide

Tobacco smoking and hepatic damage causes oxidative stress in humans and underlay numerous chronic degenerative diseases. Heavy smoking yields toxins which induce necroinflammation and increase the severity of hepatic lesions. Accordingly, the main object of this study to investigate the effect of chrysin against thioacetamide (TAA)-induced liver injury in rats exposed to tobacco smoke in combination. The study was carried out by investigating the oxidative stress biomarkers (glutathione peroxidase, SOD, catalase, paroxynase-1 and NAD (P) H: quinoneoxidoreductase 1 activities), inflammatory markers (IL-6 and IL-8 levels and gene expressions of NF-κB and VEGF) as well as histopathogical examination. It was found a significant reduction in activities of antioxidant enzymes concomitant with cytokines elevation; (IL-6 and IL-8) in rats exposed to tobacco smoke in combination with (TAA) treatment compared with normal control. Additionally, the transcript levels of (NF-κB and VEGF) genes were significantly higher in the rats exposed to tobacco smoke in combination with (TAA) treatment compared with normal control. However, treatment with chrysin, the levels of these parameters were significantly decreased which demonstrates the ameliorative effect of chrysin against (TAA) induced liver injury in rats exposed to tobacco smoke in combination. Histological investigations revealed that chrysin gave more preventive effect against thioacetamide (TAA)-induced liver injury in rats exposed to tobacco smoke. In conclusion, chrysin modulated the damage induced by exposure to tobacco smoking and
AUTOMATIC STYLE CLASSIFICATION OF JAZZ RECORDS WITH RESPECT TO RHYTHM, TEMPO, AND TONALITY

In this paper, we focus on the automatic classification of jazz records. We propose a novel approach where we break down the ambiguous task, which is commonly referred to as genre classification, into three more specific semantic levels. First, the rhythm feel (swing, latin, funk, two-beat) characterizes the basic groove organization and most often relates to the rhythm section. Second, the tonality type (functional, blues, bebop harmony) describes the general harmonic organization of the underlying composition, which serves the soloist as a guideline for the improvisation. Third, the tempo class (slow, medium, up) provides a rather broad categorization of the overall tempo of a song. We consider three individual classification tasks with respect to the different descriptors. A set of 229 jazz recordings was selected and labeled by musicology and jazz students as part of the Jazzomat Research Project. For the three tasks, we conduct several classification experiments in order to investigate the usefulness of certain features and feature groups in general as well as pre-processing methods such as feature selection and feature space transformation. For a baseline classification experiment, we use low-level audio features which are widely used in Music Information Retrieval (MIR) research. As second step, we test several recently proposed mid-level features for modeling rhythmic or harmonic content of audio data. We systematically evaluate different modifications and combinations of these features. By testing decision trees as classifiers, the obtained decision rules provide an opportunity for a musicological interpretation of the classification process. Beyond that, we also investigate which features were selected for the different tasks by an automatic feature selection algorithm. The results of this interdisciplinary study have potential implications for jazz research as well as for content-based audio analysis tasks such as music similarity search and music recommendation in general.
Microenergy acoustic pulses induced myogenesis of urethral striated muscle stem/progenitor cells.

Background Stress urinary incontinence (SUI) is a common disorder with high prevalence in women across their life span, but there are no non-surgical curative options for the condition. Stem cell-based therapy, especially endogenous stem cell therapy may be a potential treatment method for SUI. The aims of this study are to identify, isolate, and assay the function of urethral striated muscle derived stem/progenitor cells (uMDSCs) and to assess uMDSC response to microenergy acoustic pulses (MAP). Methods Urethral striated muscle was identified utilizing 3D imaging of solvent organs (3DISCO) and immunofluorescence (IF). uMDSCs were isolated and purified from Zucker Lean (ZL) (ZUC-LEAN) (ZUC-Leprfa 186) rats, with magnetic-activated cell sorting (MACS) and pre-plating methods. The stemness and differentiation potential of the uMDSCs were measured by cell proliferation, EdU, flow cytometry, IF, and Western blot. Results Comparison of the cell proliferation assays between MACS and pre-plating reveals the advantage of MACS over pre-plating. In addition, the study reveals that uMDSCs form myotubes when treated with MAP. Conclusions The uMDSCs within female rat urethral striated muscle could be a therapeutic target of MAP in managing SUI.
Management of Wilt Disease Complex Caused by Meloidogyne javanica and Fusarium Oxysporum f.sp. lycopersici on Tomato Using Some Plant Extracts

This work was conducted to study the effect of some ethanolic plant extracts i.e. lemon grass, Cymbopogon citratus, lantana, Lantana indica, camphor, Cinnamomum camphora, African marigold, Tagetes erecta, chrysanthemum , Chrysanthemum indicum and Acacia senegal at two concentrations ( 3 and 5%) on the control of wilt disease complex on tomato caused by root-knot nematode, Meloidogyne javanica and the fungus , Fusarium oxysporum f.sp lycopersici under greenhouse conditions at the Experimental Farm, Faculty of Agriculture, Menoufia University, Shibin El-Kom, Egypt. The results showed that application of plant extracts significantly reduced the disease severity, disease incidence, population of M. javanica / 100 g soil and root gall index. Furthermore, all treatments enhanced plant growth. Extract of Cymbopogon citrates 5% gave the best results in the control of target pests, followed by Cinnamomum camphora 5%. On the other hand, Acacia sengegal extract 3% showed the least effects on the tested pests.
Effect of Climate Change on Agricultural Output Growth in Ethiopia: Co-Integration and Vector Error Correction Model Analysis

This study examined the effect of climate change on agricultural output growth in Ethiopia. Co- Integration and Vector Error Correction Model estimation technique and data for the period 1981-2016 was used. Changing in annual mean temperature, annual mean rainfall, carbon dioxide emission and forest depletion were used to attribute variables for climate change. The result of vector error correction model indicate that both in the long-run and short-run, carbon dioxide emissions negatively affect agricultural output growth in Ethiopia. Annual temperature and annual rainfall negatively affect agricultural output growth in the long run and short run respectively. On the other hand, forest depletion has positive effect on agricultural output growth both long run and the short-run respectively. Policy maker should develop policies to reduce sources of carbon dioxide emissions and introduce mitigation and adaptation measures to sustain the agricultural economic growth.
Exploring the effect of vitamin D and DHA supplementation on the urine metabolome of preterm infants by 1H NMR-based metabolomics

Background and objectives: Vitamin D and docosahexaenoic acid (DHA) insufficiency and deficiency could potentially have a great impact on health outcomes in preterm infant. Due to the importance of early nutrition intervention in this population and given the lack of metabolomic studies concerning the supplementations effect on the metabolome of preterm infants, 44 premature infants were studied, divided in two groups, one receiving only vitamin D (DS) and the second both vitamin D and DHA (D-DHAS) supplementation. Two were the main objectives of the study: 1) to look at changes over time in the urinary metabolic profiles of infants before and over two months of supplementation; 2) to compare the urinary metabolome of the two groups after supplementation. Methods: 1 H NMR-based metabolomics approach was used to analyze urine samples obtained from preterm newborns at three different time points: at the time of hospital discharge and before supplementation (T0), 1 month (T1) and 2 months (T2) after the beginning of supplementation. Results: A clear temporal dynamics of the urinary metabolic profiles of preterm infants was highlighted by OPLS analysis. Both groups were characterized by growing levels of betaine, N,N -dimethylglycine, creatinine, creatine and guanidinoacetate and diminishing levels of myo-inositol and hydroxyproline with increasing postmenstrual age (PMA). Additionally, for D-DHAS citrate and dimethylamine increased, while lactate decreased over time. OPLS-DA clearly discriminated the two groups after two months of supplementation. Compared to DS, D-DHAS group was characterized by higher levels of betaine, N,N -dimethylglycine, creatinine and dimethylamine and lower amounts of lactate and myo-inositol. Conclusions: Metabolomic analysis of urine from the neonatal period could be a useful tool to understand metabolic processes linked to early nutrition and supplementation. According to our results, vitamin D supplementation exerts in preterm newborns positive effects evaluated with urinary metabolomics. Moreover, it seems that the supplementation with vitamin D and DHA exerts a higher antioxidant and protective action on newborns, and it could also positively affect the body fat composition.
Transforming Policy via Reward Advancement

Many real world human behaviors can be characterized as sequential decision making processes, such as urban travelers’ choices of transport modes and routes [1]. Differing from choices controlled by machines, which in general follows perfect rationality to adopt the policy with highest reward, studies have revealed that human agents make sub-optimal decisions under bounded rationality [2]. Such behaviors can be modeled using maximum causal entropy (MCE) principle [3]. In this paper, we define and investigate a novel reward transformation problem (namely, reward advancement): Recovering the range of additional reward functions that transform the agent’s policy from πo to a predefined target policy πt under MCE principle. We show that given an MDP and a target policy πt, there are infinite many additional reward functions that can achieve the desired policy transformation. Moreover, we propose an algorithm to further extract the additional rewards with minimum "cost" to implement the policy transformation. We demonstrated the correctness and accuracy of our reward advancement solution using both synthetic data and a largescale (6 months) passenger-level public transit data from Shenzhen, China.
CMAR_A_191969 3829..3845

Trevor S Mafu Alison V September Delva Shamley 1Division of Exercise Science and Sports Medicine, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa; 2Clinical Research Centre, Faculty of Health Sciences, University of Cape Town, Rondebosch, South Africa Introduction: Shoulder pain and disability are well-documented sequelae of breast cancer treatment. Angiogenesis signaling may have a role in the development of shoulder pain or shoulder disability in breast cancer survivors. The aim of this study was to determine if polymorphisms in angiogenesis-related genes are associated with shoulder pain or disability following breast cancer treatment. Participants and methods: A cross-sectional study was conducted on 220 South African breast cancer survivors. The study aimed to evaluate associations between shoulder pain/ disability and seven single nucleotide polymorphisms (SNPs) within five angiogenesisassociated genes: KDR (rs2305948 C>T; rs7667298 C>T), NOS3 (rs1549758 C>T), MMP2 (rs708269 A>T), THBS2 (rs9766678 A>G) and TIMP3 (rs5754312 T>A; rs715572 G>A). In addition, associations between shoulder pain/disability and inferred haplotypes for KDR and TIMP3 SNPs were evaluated. Participants were grouped into no–low and moderate–high shoulder pain/disability based on total pain/disability scores: ≤30 and >30, respectively using the shoulder pain and disability index (SPADI). Results: No independent associations with shoulder pain/disability categories were found for all SNPs. However, 1 inferred haplotype (KDR “TT”) differed significantly (P=0.014) between the shoulder disability categories. After adjusting for participants’ age, the differences in KDR inferred haplotype frequencies between shoulder disability categories became non-significant (P=0.052). Conclusion: Our findings provide a preliminary suggestion of a possible association between polymorphisms in genes involved in angiogenesis and the presence of moderate– high shoulder disability among South African breast cancer survivors. A larger prospective cohort study is currently being conducted by our group.
Brain-computer interface (BCI) for stroke rehabilitation : effects of optimal electrode channel selection and tactile feedback in chronic stroke patients

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A Reduction from Reinforcement Learning to No-Regret Online Learning

We present a reduction from reinforcement learning (RL) to no-regret online learning based on the saddle-point formulation of RL, by which "any" online algorithm with sublinear regret can generate policies with provable performance guarantees. This new perspective decouples the RL problem into two parts: regret minimization and function approximation. The first part admits a standard online-learning analysis, and the second part can be quantified independently of the learning algorithm. Therefore, the proposed reduction can be used as a tool to systematically design new RL algorithms. We demonstrate this idea by devising a simple RL algorithm based on mirror descent and the generative-model oracle. For any $\gamma$-discounted tabular RL problem, with probability at least $1-\delta$, it learns an $\epsilon$-optimal policy using at most $\tilde{O}\left(\frac{|\mathcal{S}||\mathcal{A}|\log(\frac{1}{\delta})}{(1-\gamma)^4\epsilon^2}\right)$ samples. Furthermore, this algorithm admits a direct extension to linearly parameterized function approximators for large-scale applications, with computation and sample complexities independent of $|\mathcal{S}|$,$|\mathcal{A}|$, though at the cost of potential approximation bias.
Deep Clustering by Gaussian Mixture Variational Autoencoders With Graph Embedding

We propose DGG: {\textbf D}eep clustering via a {\textbf G}aussian-mixture variational autoencoder (VAE) with {\textbf G}raph embedding. To facilitate clustering, we apply Gaussian mixture model (GMM) as the prior in VAE. To handle data with complex spread, we apply graph embedding. Our idea is that graph information which captures local data structures is an excellent complement to deep GMM. Combining them facilitates the network to learn powerful representations that follow global model and local structural constraints. Therefore, our method unifies model-based and similarity-based approaches for clustering. To combine graph embedding with probabilistic deep GMM, we propose a novel stochastic extension of graph embedding: we treat samples as nodes on a graph and minimize the weighted distance between their posterior distributions. We apply Jenson-Shannon divergence as the distance. We combine the divergence minimization with the log-likelihood maximization of the deep GMM. We derive formulations to obtain an unified objective that enables simultaneous deep representation learning and clustering. Our experimental results show that our proposed DGG outperforms recent deep Gaussian mixture methods (model-based) and deep spectral clustering (similarity-based). Our results highlight advantages of combining model-based and similarity-based clustering as proposed in this work. Our code is published here: https://github.com/dodoyang0929/DGG.git
2L-3W: 2-Level 3-Way Hardware-Software Co-Verification for the Mapping of Deep Learning Architecture (DLA) onto FPGA Boards

FPGAs have become a popular choice for deploying deep learning architectures (DLA). There are many researchers that have explored the deployment and mapping of DLA on FPGA. However, there has been a growing need to do design-time hardware-software co-verification of these deployments. To the best of our knowledge this is the first work that proposes a 2-Level 3-Way (2L-3W) hardware-software co-verification methodology and provides a step-by-step guide for the successful mapping, deployment and verification of DLA on FPGA boards. The 2-Level verification is to make sure the implementation in each stage (software and hardware) are following the desired behavior. The 3-Way co-verification provides a cross-paradigm (software, design and hardware) layer-by-layer parameter check to assure the correct implementation and mapping of the DLA onto FPGA boards. The proposed 2L-3W co-verification methodology has been evaluated over several test cases. In each case, the prediction and layer-by-layer output of the DLA deployed on PYNQ FPGA board (hardware) alongside with the intermediate design results of the layer-by-layer output of the DLA implemented on Vivado HLS and the prediction and layer-by-layer output of the software level (Caffe deep learning framework) are compared to obtain a layer-by-layer similarity score. The comparison is achieved using a completely automated Python script. The comparison provides a layer-by-layer similarity score that informs us the degree of success of the DLA mapping to the FPGA or help identify in design time the layer to be debugged in the case of unsuccessful mapping. We demonstrated our technique on LeNet DLA and Caffe inspired Cifar-10 DLA and the co-verification results yielded layer-by-layer similarity scores of 99\% accuracy.
Many-body scar state intrinsic to periodically driven system

The violation of the Floquet version of eigenstate thermalization hypothesis is systematically discussed with realistic Hamiltonians. Our model is based on the PXP type interactions without disorder. We exactly prove the existence of many-body scar states in the Floquet eigenstates, by showing the explicit expressions of the wave functions. Using the underlying physical mechanism, various driven Hamiltonians with Floquet-scar states can be systematically engineered.