Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloning and expression analysis of zygote arrest 1 ( Zar 1 ) in New Zealand white rabbits

Zygote arrest 1 (Zar1) is an oocyte-specific maternal-effect gene. Previous studies indicate that Zar1 plays important role in early embryo development, but little is known about its function in rabbit. The objectives of this study were to clone the New Zealand white rabbit Zar1 gene and to investigate its expression in various organs in groups of animals with different reproductive traits. We obtained a 709-bp Zar1 cDNA fragment consisting of an 8-bp exon 1, 161-bp exon 2, 75-bp exon 3, 271-bp exon 4 and 194-bp 3′ sequences. The rabbit Zar1 nucleotide sequence showed per cent identities of 91, 88, 88, 87, 86, 87, 76 and 82% with Zar1 orthologues in human, cattle, sheep, pig, mouse, rat, zebrafish and Xenopus laevis, respectively, indicating a high homology with other species and evolutionary conservation. Quantitative real-time polymerase chain reaction analyses revealed nonoocyte-specific Zar1 expression, with expression in spleen, lung, ovary, uterus, heart, liver and kidney. The expression level was highest in the lung. This study may lay the theoretical foundation for the study of ZAR1’s biological
VehicleNet: Learning Robust Feature Representation for Vehicle Re-identification

Vehicle re-identiﬁcation (re-id) remains challenging due to signiﬁcant intra-class variations across different cameras. In this paper, we present our solution to AICity Vehicle Re-id Challenge 2019. The limited training data motivates us to leverage the free data from the web and deploy the two-stage learning strategy. The success of large-scale datasets, i.e . , ImageNet, inspires us to build a large-scale vehicle dataset called VehicleNet upon the public web data. Speciﬁcally, we combine the provided training set with other public vehicle datasets, i.e . , VeRi-776, CompCar and Ve-hicleID as VehicleNet. In the ﬁrst stage, the training set is scaled up about 16 times, from 26,803 to 434,453 images. Despite the bias between different datasets, e.g . , illumination and scene, VehicleNet generally provides the common knowledge of the vehicle, beneﬁting the deeply-learned model in learning the invariant representation towards different viewpoints. In the second stage, we further ﬁne-tune the trained model only on the original training set. The second stage intends to minor the gap between VehicleNet and the original training set. Albeit simple, we achieve mAP 75.60% on the private testing set without extra information, e.g . , temporal or spatial annotation of test data.
CURRENT STATE OF BENCHMARKING SPECTRUM SENSING AND ROUTING STRATEGIES IN COGNITIVE RADIO AD HOC NETWORKS

[[[[[[[[[[[[[[[[[[[[[[ Cognitive radio technology is one of the prominent solutions developed to address under-utilization of spectrums. The CR technology provides different solutions by exploring different segments in the spectrum based on the opportunities created due to the temporary vacation of licensed users. Ease of networking the devices equipped with cognitive abilities for creating CRAHNs networks poses several challenges at different layers of the network. These challenges arise primarily due to the inherent flexibility in accessing spectrum by multiple devices. This paper offers a survey of the contemporary spectrum sensing and routing solutions in CRAHNs . The report commences through listing the spectrum sensing and routing challenges which are linked to the CRAHNs . In the next sections, the research work discusses multiple routing protocols and sensing protocols mostly designed for the cognitive radio network. These protocols can be broadly classified into six groups on the basis of routing metrics. The routing metrics considered for this analysis includehop/delay, throughput performance, reasonable stability, energy consumption awareness along with multimetric protocols. These multi-metric protocols either unite multiple metrics or engage different metrics as per specific rules. The paper also offers a discussion of the directions which ought to be taken by the future research.
Extraction and Analysis of Spatial Correlation Micrograph Features for Traceability in Manufacturing

We propose a method for ensuring traceability of metal goods in an efficient and secure manner that leverages data obtained from micrographs of a part’s surface that is instance specific (i.e., different for another instance of that same part). All stakeholders in modern supply chains face a growing need to ensure quality and trust in the goods they produce. Complex supply chains open many opportunities for counterfeiters, saboteurs, or other attackers to infiltrate supply networks, and existing methods for preventing such attacks can be costly, invasive, and ineffective. The proposed method extracts discriminatory-yet-robust intrinsic strings using features extracted from the two-point autocorrelation data of surface microstructures, as well as from local volume fraction data. By using a synthetic dataset of three-phase micrographs similar to those obtained from metal alloy systems using low-cost optical microscopy techniques, we discuss tailoring the method with respect to cost and security and discuss the performance of the method in the context of anticounterfeiting and how similar methods may be evaluated for performance. Cryptographic extensions of this methodology are also discussed.
Uncertainty-Guided Continual Learning in Bayesian Neural Networks - Extended Abstract

Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot access data from previous tasks and when the model has a fixed capacity. Current regularization-based continual learning algorithms need an external representation and extra computation to measure the parameters’ importance. In contrast, we propose Bayesian Continual Learning (BCL), where the learning rate adapts according to the uncertainty defined in the probability distribution of the weights in networks. We evaluate our BCL approach extensively on diverse object classification datasets with short and long sequences of tasks and report superior or on-par performance compared to existing approaches. Additionally we show that our model can be task-independent at test time, i.e. it does not presume knowledge of which task a sample belongs to.
Effect of Entrepreneurial orientation on business performance moderated by Government Policy ( Study On SMEs In Timor Leste )

This study purpose is to analyze and explaining effect of entrepreneurial orientation on business performance with government policy as a moderating variable. This research was conducted at capital city of Dili, Baucau and Maliana District outside 13 districts in Timor Leste. This study populations are 275 SMEs from three districts (Dili, Baucau and Maliana) in Republica Democratica de Timor-Leste (Timor Leste) State. This study uses saturated samples. There are 157 questionnaires collected with response rate is 57.1%. Data Analysis Techniques is Generalized Structured Component Analysis (GSCA). Study result shows that entrepreneurial orientation affect on on business performance. It means better entrepreneurial orientation can improve performance of small and medium enterprises. Government policy can not moderate effect of entrepreneurial orientation on business performance. It shows that government policy does not have direct effect and moderating effect to improve SMEs performance.
A Locality Aware City-Scale Multi-Camera Vehicle Tracking System

Vehicle tracking across multiple cameras can be difficult for modern tracking systems. Given unlikely candidates and faulty similarity estimation, data association struggles at city-scale tracking. In order to avoid difficulties in a large scenario, we keep the tracking procedure within a minimal range. The benefit of this smaller scenario idea is two-fold. On the one hand, ruling out most unlikely candidates decrease the possibility of mis-assignment. On the other hand, the system can devote all its discriminative power on the remaining local candidate pool. In fact, our tracking system features two parts to keep the data association within a small range, while at the same time increase the locality awareness for smaller scenarios. First, multiple cues including spatial-temporal information and camera topology are leveraged to restrict the candidate selection. Second, the appearance similarity estimation module is carefully tuned so that it focuses on the smaller local candidate pool. Based on a minimal view for the large scenario, the proposed system finished 5 place in the 2019 AI-City challenge for city-scale multi-camera vehicle tracking.
Resource Allocation Scheme Based on Neural Network and Group Search Optimization in Cloud Environment

In cloud environment, all kinds of idle resources can be pooled to establish a resource pool, and different kinds of resources can be combined as a service to the users through virtualization. Therefore, an effective scheme is necessary for managing and allocating the resources. In this paper, economic and intelligent methods are employed to form an intelligent resource allocation scheme based on double combinatorial auction with respect to the characteristics of resources in cloud environment. In the proposed scheme, a reputation system on the basis of quality of experience (QoE) is devised, and the reputation attenuation coefficient and the user credit degree are introduced to decrease the negative effects of malicious behaviors on resource auctions, providing QoE support to resource dealing. In order to determine bidding price rationally, a bidding price decision mechanism based on back propagation (BP) neural network is presented to comprehensively consider various influence factors to make price adapt to the fluctuating market. Finally, due to the fact that the problem of winner determination in combinatorial auction is NP-complete, a group search optimization algorithm is adopted to find ∗ 基金项目: 国家杰出青年科学基金(61225012, 71325002); 教育部高等学校博士学科点专项科研基金(20120042130003); 中央 高校基本科研业务费专项资金(N110204003, N120104001) 收稿时间: 2012-12-26; 修改时间: 2013-6-26, 2013-09-09; 定稿时间: 2013-12-09 孙佳佳 等:云环境下基于神经网络和群搜索优化的资源分配机制 1859 the specific resource allocation solution with market surplus and total reputation optimized. Simulation studies are conducted to demonstrate the feasibility and effectiveness of the proposed scheme.
Feed additives ( enzymes ) , as a way for improving performance parameters of gilt and weaned piglets

The major part of phosphorus (P) in cereals and seeds is stored as phytate (myo-inositol hekxakisphosphate), that is hard to digest for pigs. Phytate P in plants is a mixed calcium-magnesium-potassium salt of phytic acid that is present as chelate and solubility is very low. Phosphorus in this form is poorly digestible/available for simple-stomached animals. However, the enzyme phytase catalyses the hydrolysis of phytate rendering phosphate available for absorption. The reported effects of phytase addition on the improvement of P digestibility show great variation. The aim of this study was to test the effects of the microbial phytase (NATUPHOS) on the performance parameters of gilt and weaned piglets. The microbial phytase preparation (Aspergillus niger, NATUPHOS) was supplemented to a basal ration 750 FTU/kg feed and the effects on growth performance of gilt and weaned piglets were studied weekly. The supplementation of microbial phytase improved slightly daily weight gain, feed intake and feed conversion ratio. Overall a positive effect of the microbial phytase on performance parameters was observed. The Pexcretion in the faeces was reduced also.
Recent developments in multibeam echo-sounder processing – The multibeam potential for sediment classification and water column sound speed estimation

The multi-beam echo-sounder (MBES) system allows for unprecedented performance in mapping seaand river-floors with a 100% coverage. It measures with a single acoustic ping the water depths along a wide swathe perpendicular to the ship track using the travel times of the echo signals received in the acoustical beams. MBES ping rates depend on the water depth, but typically are a few tens of Hz. The MBES opening angle is about 150 degrees and contains several hundred narrow beams, thereby providing high-resolution bathymetric maps. The bathymetry is obtained on-line. Frequently, however, knowledge about the water column sound speeds is insufficient for correctly converting the measured travel times to depths. Here, we present a method for estimating the unknown sound speeds from the MBES data itself. This method can be applied as soon as data along overlapping swathes become available. For this semi-online processing, efficient optimization approaches have been implemented. In addition to the travel times, the MBES also provides measurements of the backscatter strengths in each beam, which are known to contain information about the sediment types. We show results of applying a model-based classification method that employs the MBES backscatter data and discriminates between sediments in the most optimal way. The method has been applied for classification of sediments in a large number of areas. Here, we will show classification results for parts of the river Waal and the North Sea.
Perceived stress and psychological distress among chinese physicians

Abstract Although the association between perceived stress and psychological distress has been demonstrated, the mechanism behind the association is not well understood in physicians. The purpose of this study was to examine how coping styles (positive and negative) mediated the association between perceived stress and psychological distress among Chinese physicians. A cross-sectional survey was conducted in Liaoning Province, China, between October and December 2017. Self-administered questionnaires, including the General Health Questionnaire to assess psychological distress, the Stress Reaction Questionnaire to assess perceived stress, and the Trait Coping Style Questionnaire to assess coping style, as well as surveys of demographic and occupational characteristics, were distributed to 1120 physicians employed in large general hospitals. The final sample consisted of 1051 participants. Asymptotic and resampling strategies were used to examine how coping style mediated the association between perceived stress and psychological distress. Perceived stress was significantly and positively associated with psychological distress among physicians. Both positive and negative coping significantly mediated the association between perceived stress and psychological distress. For psychological distress and its 3 dimensions (depression, anxiety, and reduced self-affirmation), the proportions of mediating roles of coping styles were 26.1%, 29.9%, 24.8%, and 22.7%, respectively. Perceived stress had positive effects on psychological distress, and coping style was a mediator in this relationship among Chinese physicians. In addition to reducing stress in clinical practice, appropriate coping styles should be adopted in psychological distress prevention and treatment strategies.
B Bone Chemistry and Ancient Diet

Dietary practices are a fundamental aspect of human life, from the basic caloric necessities to the socioeconomic variables which affect food production, acquisition, preparation, and consumption and how that may vary seasonally as well as over an individual’s lifetime. Archaeologists have used many different data sources to study ancient dietary practices, including direct evidence from animal bones, macrobotanical plant remains, pollen and phytoliths in the soil, organic residues in pottery, and coprolites, as well as indirect evidence from skeletal pathology, dental wear patterns, ethnographic observations, writings, and artistic depictions. Nevertheless, for most such studies, the results are just the determination of the main menu, with animal foods the only source of semiquantitative dietary estimates. It was only in the late 1970s that a new area of research developed – bone chemistry – which has expanded considerably our understanding of human dietary practices (Vogel and van der Merwe 1977). Biochemically, you are what you eat, and bones, teeth, and other tissues preserved in the archaeological record may be analyzed and provide direct information about the diet of individuals. This has led to research on how diets may vary based on age, sex, and socioeconomic status, as well as between different sites and time periods (here are some of the recent synthetic publications about stable isotope analyses and human diet: Tykot 2004, 2010; Pollard and Heron 2008; LeeThorp 2008; Price and Burton 2011; Makarewicz and Sealy 2015). Specifically, the combination of stable carbon and nitrogen isotopes in bone collagen and other tissues, and carbon and oxygen isotopes in bone apatite or tooth enamel, may be used to reconstruct prehistoric diet because of differential isotope fractionation between certain plant groups of atmospheric carbon dioxide during photosynthesis and trophic level increases in nitrogen isotopes. This allows us to distinguish between plants that follow different photosynthetic pathways; those which are nitrogen fixing versus absorption; and consumption of foods from different trophic levels, especially aquatic fish and mammals versus terrestrial plants and animals, but also between hunter-gatherers and agriculturalists, and even address short-term dietary change through microsampling of tooth roots and hair. Trace elements such as barium and strontium provide additional information about food sources and diet, while oxygen isotope analysis is used to study potential seasonality of shellfish
HOOGHOUDT'S DRAINAGE EQUATION, ADJUSTED FOR ENTRANCE RESISTANCE AND SLOPING LAND

Hooghoudt's drainage equation for flat land is adjusted in this article to cover the drainage of sloping land by horizontal and parallel subsurface pipe drains with entrance resistance. The drains are laid along the contours at equal depth below the soil surface, which receives a steady recharge evenly distributed over the area. The adjusted equations are tested against the results obtained with a numerical method of finite elements described by Fipps and Skaggs (1989), and against the results of sand tank experiments described by Zeigler (1972). The tests show a good agreement. In practice, the adjusted Hooghoudt equation is easier to apply than the numerical and scale models.
Model Based Conditional Gradient Method with Armijo-like Line Search

The Conditional Gradient Method is generalized to a class of non-smooth non-convex optimization problems with many applications in machine learning. The proposed algorithm iterates by minimizing so-called model functions over the constraint set. Complemented with an Armijo line search procedure, we prove that subsequences converge to a stationary point. The abstract framework of model functions provides great flexibility for the design of concrete algorithms. As special cases, for example, we develop an algorithm for additive composite problems and an algorithm for non-linear composite problems which leads to a Gauss–Newton-type algorithm. Both instances are novel in non-smooth non-convex optimization and come with numerous applications in machine learning. Moreover, we obtain a hybrid version of Conditional Gradient and Proximal Minimization schemes for free, which combines advantages of both. Our algorithm is shown to perform favorably on a sparse non-linear robust regression problem and we discuss the flexibility of the proposed framework in several matrix factorization formulations.
Catchment topography and the distribution of electron donors for denitrification control the concentration of nitrate in headwater streams of the Lake Hachiro watershed

We examined the linkages between topography and electron donors for denitrification in headwater streams in the Lake Hachiro watershed that has marine sedimentary rocks. In 33 headwater streams, we sampled water at the catchment nine times in 2 years. Stream sediment was sampled once for measurement of denitrification potential (DP), water-extractable soil organic carbon (WESOC), and easily oxidizable sulfide (EOS), which are considered the principal potential electron donors for denitrification. The topographical features of each catchment were calculated using a digital elevation model with 10-m grid cells. Stream NO3 concentrations displayed large spatial variation among catchments, ranging from 0.06 to 0.52 mg N L, and were significantly positively correlated with slope in the catchments (r = 0.663, P < 0.01, n = 33), indicating that NO3 was removed to a greater extent in gentle slope catchments. Generalized linear model showed slope, slope aspect, sediment DP, and EOS significantly affected in-stream NO3 concentration. Stream SO4 concentrations tended to increase as NO3 concentrations decreased and EOS contents increased, indirectly indicating sulfurmediated denitrification. NO3 reduction with SO4 production, and sulfur-oxidizing bacteria was detected in the stream bank soil with high EOS content. We conclude that catchment topography and the distribution of electron donors in riverbed sediment explain the spatial variation in in-stream NO3 concentration and, by inference, catchment denitrification. These results indicate that more NO3 may be denitrified by sulfurmediated denitrification owing to the abundance of sulfides in the catchment from marine sedimentary rocks.
Continuous-Time Controllers for Robust Stabilization of 3 D Bipedal Walking

This work presents a systematic approach to design continuous-time feedback laws to robustly and exponentially stabilize periodic orbits for legged robots. A parameterized family of continuous-time feedback laws is considered. By investigating the properties of the Poincaré map, a sensitivity analysis is presented to translate the robust stabilization problem into a set of Bilinear Matrix Inequalities (BMIs). A BMI optimization problem is then setup to tune the parameters of the continuoustime controllers.
The Campanian Grünbach Flora of Lower Austria: palaeoecological interpretations

Palaeoecology of Grünbach wetlands is reconstructed based on fossil plant morphology, anatomy and taphonomic observations. Five plant communities have been reconstructed: aquatic plant community, swamp-semiaquatic plant community, juglandaceous and palm wetland forest, riparian community and mesophytic forest community.
Robotic Arm Control Using Deep Reinforcement Learning with Gaussian Extrinsic Rewards

In this project, we aim to produce a control policy for a Kuka robotic arm in achieving grasping randomly placed objects with various shapes. For the baseline, we implemented a Double Deep Q-network (Double DQN) [1] in discrete action space. Subsequently, we implemented a Double Deep Q-network with prioritized experience replay (Double DQN + PER) which surpasses the performance of the baseline in discrete action space. We then moved our focus to continuous action space, where we explored several algorithms including Deep Deterministic Policy Gradients (DDPG), Deep Deterministic Policy Gradients with Prioritized Experience Replay (DDPG + PER) and Deep Deterministic Policy Gradients with Hindsight Experience Replay (DDPG + HER). The performance of these aforementioned methods all exceeds the baseline.